Jacobs J. et al. Direct recordings of grid-like neuronal activity in human spatial navigation. Nature Neuroscience, 2013

Do you happen to have a poor sense of direction? Do you often find yourself holding a map upside-down? Well, now you can blame your grid cells. Using direct human brain recordings, researchers have identified a novel type of cell in the brain that helps people keep track of their relative location while navigating through an unfamiliar environment. Scientists got this rare opportunity to identify these unique cells while they studied brain recordings of epilepsy patients via electrodes implanted deep inside their brains. These cells have been called “grid cells” because they are activated in a triangular grid pattern. The “grid cell” is distinct among brain cells because its activation represents multiple spatial locations, which allows the brain to keep track of navigational cues, such as how far you are from a starting point or your last turn. This type of navigation is called path integration. During brain recordings, 14 study participants were asked to play a video game where they ride a virtual bicycle to navigate from one point to another to retrieve objects and then recall how to get back to the places where they found the objects. While participants were playing the game, researchers examined the relation between navigation and the corresponding activity of individual neurons. Results were striking: each grid cell responded at multiple spatial locations that were arranged in the shape of a grid suggesting that the navigation information is principally encoded in our brains through this triangular grid pattern. Without grid cells, humans would frequently get lost or have to navigate based solely on landmarks. Differences in how well the grid cells work could potentially explain why some people have a better sense of direction than others. In addition, grid cells are located in the entorhinal cortex which is a critical component of human memory. The entorhinal cortex is also the first brain region affected in Alzheimer’s disease. Thus, understanding how grid cells work could potentially help us to understand why people with Alzheimer’s frequently become disoriented as well as to develop new strategies to improve brain function in the affected individuals.

Giant viruses open Pandora’s box

Philippe N. et al. Pandoraviruses: amoeba viruses with genomes up to 2.5 Mb reaching that of parasitic eukaryotes. Science, 2013 Jul 19

On a fairly ordinary day, two French biologists were analyzing water samples collected off the coast of Chile. What they saw under the microscope was quite amazing: a previously unidentified organism, about the size of a bacterial cell, appeared as a large dark spot. Astonishingly, these new organisms seemed to be infecting and killing the amoeba in the water. Later, another group of researchers found a similar organism in a pond in Australia. Both groups soon realized that they discovered a type of “giant” virus which is at least twice as big as the largest known viruses. The biggest virus discovered so far was called Mimiviruses, with a size of 700 nanometers and carrying more than 1000 genes. The newly discovered viruses are called Pandoraviruses, which are 1 micrometer long and 0.5 micrometers across. Pandoraviruses are visible under a light microscope and contain more than 2500 genes. A viral genome consisting of 2500 genes is extremely large compared to known viruses, such as the Influenza or HIV, which only contain 10 genes or less. More importantly, 93% of the genes did not resemble any known lineage in the natural world, suggesting Pandoraviruses are not related to any known virus family and may represent a new life form. These findings generated new perspectives about how scientists see viruses. It raised the possibility that there might be many different kinds of giant viruses out there to be discovered. Some biological features in these giant viruses could easily blur the line between life forms and viruses, which are considered to be non-living. Although Pandoraviruses do not infect human cells, there might be other giant viruses out there that could infect human cells. There are still a lot of human diseases known to have an infectious component but for which no infectious agent has been identified yet. This study will definitely encourage people to actively look for the role of giant viruses in some diseases.

See through the Ears

Haigh A. et al. How well do you see what you hear? The acuity of visual-to-auditory sensory substitution. Frontiers in Psychology, 2013 Jun 18

Scientists have created a revolutionary device for the blind that allows them see the world through their ears. The device “vOICe” trains the brain to invoke mental images of what they are hearing around them. The first test trial of vOICe has been performed on blindfolded sighted people. The participants took a standard eye test where they were asked to view the letter E turned in four directions and in various sizes. The best visual acuity is considered 20/20 (distance in feet/size of the E) and the majority of participants were able to achieve the best performance possible, nearly 20/400 sight. This is an impressive result when compared to an alternative stem-cell based sight restoration technique, which only yielded 20/800 visual acuity. In addition, the affordable and non-invasive nature of vOICe would offer a unique option. But, how might this work in practice? One can imagine that visually-impaired people would wear a discreet head-mounted camera such as Google Glass and receive wireless audio information through mini earbuds. As the person turns to look in various directions, the device scans images and correlates those with soundscapes, then the person’s brain would momentarily translate those into mental images of the objects — like braille for the ears. These sensory substitution devices could potentially be employed in combination with other alternative invasive techniques to train the brain to see again, or even to see for the first time.

Human beings are visually-oriented in their daily life; they use the sense of sight more than any of the other senses with which they have been endowed. The modern understanding of human vision and the underlining principles were only discovered in the past couple centuries. The nineteenth and twentieth centuries witnessed the development of photographic and digital imaging camera systems, which partially mimic human visual systems. We will open a small window on the history of human vision and camera systems, and try to compare today’s state-of-the-art cameras with the human visual system, focusing mainly on solid-state image sensors, or camera chips, and the image-sensing element of the human visual system, the eye.

History of human vision

Human vision has been the subject of conflicting interpretations since ancient times. Many ancient physicians and philosophers believed in the theory of extramission, or the active eye. According to this theory, the eye perceives objects by emanating light and seizing objects with its rays. It was in medieval Islamic culture that research on human vision and optics developed into a system similar to the modern theory of vision. Among others, Ibn Al-Haytham (Alhazen) (965-1040 A.D.), a Muslim physicist, astronomer, and mathematician in the tenth century, played a great part in this field by promoting the intromission theory which states that vision only occurs because of light rays entering the eye. Ibn Al-Haytham founded physiological optics, which distinguished the functioning of the eye from the behavior of light. On the other hand, ten centuries after Ibn Al-Haytham, Winer et al. (2002) have found recent evidence that as many as 50% of American college students believe in the extramission theory.1

Although the fundamental features, anatomy, and physiology of the eye were documented by Galen (129–200 A.D.), an ancient Greek physician, in the second century A.D., it was Kepler, a close reader of Ibn Al-Haytham, who offered the first theory of the retinal image and the correct operation of the eye in 1604. He proclaimed, “Therefore vision occurs through a picture of the visible things on the white, concave surface of the retina.” Progress came slowly after Kepler, because little was known about the nervous system until the nineteenth century, and only recently have scientists acquired a more knowledge about how the brain apprehends the retinal image. But many questions still elude us.

History of the camera

In parallel with curiosity about human vision, human beings have also tried to mimic human vision by capturing images of objects with instruments. Around 1000 A.D., Ibn Al-Haytham, also known as the father of modern optics, invented the pinhole camera,2 and explained why the image was upside down. It was Johannes Kepler who further suggested the use of a lens to improve the pinhole camera in the 1600s. Capturing an image on a photographic plate was first achieved in the early 1800s. Consequently, photographic cameras began to be mass-marketed in the twentieth century. The photographic equipment with which we are all familiar today, such as the 35mm camera, flash bulb, Polaroid camera, and the point-and-shoot auto focus camera, all were developed in the twentieth century. The invention of the camera as we know it today paved the way for other technologies, including the moving image capture, and, later, the digital camera, in which electronic image-capture devices were used. In 1972, chemically processing an image onto photographic paper no longer became the sole destination of an image, because the first filmless electronic camera was patented by Texas Instruments Corporation. Filmless electronic cameras were made possible with the invention of solid-state image-capture devices called charge coupled devices (CCD) and metal-oxide-semiconductor (MOS) image sensors in the late 1960s. Since the invention of solid-state imagers, people have become more visually stimulated and oriented than ever before in history.

A comparison of camera chips and the human eye

The technological advancements of solid-state image-capture camera chip design and manufacturing during the past twenty-five years has made digital imaging more affordable and accessible to the general public. These advancements have become more visible to consumers in mobile products, particularly in cellular phones, in which there are still and video-camera functions. Although digital cameras are easily available today, the state-of-the-art image sensor chips used in these cameras exhibit a performance gap when compared with the capabilities of the human eye. How good these image sensor chips are today when compared to our eyes is a question that will be elaborated on.

It is possible to compare the capabilities of the human eye and state-of-the-art image sensor chips used in cellular phones or in mainstream PC and digital still cameras. It is also possible to compare the capabilities of the human visual system, including the eyes, the optic nerve, the visual cortex, etc. with a digital camera system which includes optics, image-capture and signal-processing chips and other camera apparatuses. The capabilities include ability to see different colors (spectral response), photo-element (pixel) characteristics (size, density, distribution), light sensitivity, light-intensity response range, functionality and operation modes, and signal processing capabilities.

Spectral response

A single light-sensing element in a solid-state image sensor is called a pixel. In the human eye it is called the photoreceptor. Both elements convert impinging light or photons into electrical signals. The human eye sees in the so-called visible spectrum, between 380nm (blue) and 750 nm (red), and utilizes two kinds of photoreceptors on the retina; rods and cones. The cones are used for color and daylight vision. Rods are responsible for night vision. There are three types of cone photoreceptors on the retina that contain different types of photosensitive pigments. The three types of cones are L, M, and S, and they have pigments that respond best to wavelengths of light that are long or red (peak at 564 nm), medium or green (peak at 534 nm), and short or blue (peak at 420 nm), respectively. The rods (R) are most sensitive at a wavelength of approximately 498 nm (green), as seen in Figure 1.3 Image sensor pixels in digital cameras mimic the photoreceptors in the human eye for color vision. They utilize three kinds of color filters (red, green, blue) on top of each pixel to convert light rays into electrical signals in different visible spectrums. Unlike the cones in the human eye, camera pixels and color filters can be designed to cover wide spectrums that are not visible to the human eye, for instance, the x-ray, ultraviolet, and infrared spectrums. In the category of spectral response range, camera pixels exhibit greater flexibility than those of the photoreceptors of the human eye. On the other hand, interestingly enough, the eyesight that humans possess has similar spectral characteristics as the sun. The solar light emission peaks in the visible spectrum as seen in Figure 2.4

Figure 1. Spectral absorption curves of the short (S), medium (M), and long (L) wavelength pigments in human cone and rod cells.3

Figure 2. The daylight solar spectral power distribution on earth.4

Pixel and array size

The size of pixels in today’s modern digital cameras is getting closer to the size of the photoreceptors in human eye. The typical human eye contains an average of 130 million photoreceptors. The diameter of the rods and cones varies between 1.0m and 8.0m, depending on their location on the retina.5 Today’s state-of-the-art image sensor chips contain 10 to 30 million pixels. Each pixel can be as small as 1.4m in diameter. To date there has been no image sensor that is 1.4m pixel in size or more than 8 million pixels. However, the human being has been equipped with photoreceptors that are as small as 1.0m and has more than 100 million photoreceptors; and this is since the beginning of existence. It is also estimated that the resolution of the human eye is equivalent to an imager sensor chip of 576 million pixels with a 120 degree field of view.6 Thus we still have a long way to go in improving the image-sensor pixel and array sizes used in cameras if we are to match the human eye.

Pixel distribution and formation

In the human eye the photoreceptor size and densities change, depending on their location on the retina. For example, no rods exist on the focus center of the eye, which is called the fovea. Color vision photoreceptors, which total only 10% of the eye’s photoreceptors, are located mostly on the fovea. There is an irregular distribution of photoreceptors which is unique for every human being, like a fingerprint. Yet, we all see things the same, such as colors (with the exception of people who are colorblind). In camera chips, however, pixels are arrayed regularly, in two-dimensions. As the image-processing techniques and algorithms used in camera systems are linear and do not closely mimic the signal processing that exists in the human visual system, regularly arrayed pixels are required.

Light sensitivity and response range

Although the pixel sizes in image-sensor chips are approaching the size of the photoreceptors in the human eye, camera systems are not yet close to being able to match performance in terms of light sensitivity and response range. The human visual system and photoreceptors can easily adapt to very dim and bright light, with a light-intensity response range of ten billion to one (1010:1).7 This response range goes from light conditions on a bright sunny day to dim night vision. Typically, a conventional consumer camera pixel has a light intensity response range of one thousand to one (103:1).8 In a camera system, details of a captured scene are either concealed in the dark regions or washed out by the bright light, depending on the exposure settings of the system. Thus, one could say that the human visual system works ten million times (107) more efficiently than that of consumer cameras in terms of transferring scenes into images.

Operation principle

In terms of operation principles, the photoreceptors in the human eye convert light rays into electrical signals with extremely rapid electro-chemical reactions which can detect a single photon. Typically, in the image sensor pixel of a digital camera the photoelectric effect is used to convert impinging photons into electrical charges. Electrical charges are collected and stored in each pixel during the exposure period. Collected electric charges in each pixel are amplified and converted into digital ones (logic-1) and zeros (logic-0) during image readout before the image is sent to higher processing elements, such as a personal computer, digital-still or video camera. It is possible for a single photon-counting camera to be developed. However, very special and larger pixel sizes and extra apparatuses are required to build such a camera system. Thus, we could say that it is almost impossible to build imaging pixels that have the capability and dimensions of the photoreceptors of the human eye with today’s state-of-the-art technology.

Signal processing capabilities

The captured image in the human eye is preprocessed before it is sent to the visual cortex of the brain. This preprocessing consists of a data reduction operation in which nothing is lost, with a compression ratio of 130 to 1, as only 1 million optic nerves leave each eye carrying the information from 130 million photoreceptors. This compression allows the brain to process information at a rate of 25 to 150 scenes or frames per second. Typically, every pixel in an image sensor chip is first transferred to higher processing units. A data compression method is either carried out with some loss of details in the image or the compression is never used. The transfer of frames in camera chips typically takes place sequentially, reducing the speed of the image-capture operation or frame rate. Different techniques are used to maintain a capture rate of, at most, 25 frames-per-second in camera chips. With today’s technology, image sensors that have a capture rate of one million frames per second have been proposed and can be manufactured for scientific applications.

The inherent inefficiencies of image-capture in today’s image-sensor chips are hidden by employing the limitations of the human eye. For example, solid-state image sensors have always been produced with row or column-vice uncanny stripes which are easily picked up by the human eye. However, psycho-visual experiments have shown that the human eye can only detect contrasts between two adjacent gray lines when the difference is greater than 0.5%. Thus, if a camera chip is designed to have a column to column or row to row contrast of less than 0.5%, these odd stripes would not be visible.

Conclusion

Humans are visually oriented and without a doubt, our eyes are considered to be our primary source of information. It is obvious that the human visual system is extremely complex and this complexity has fascinated human beings throughout history. Yet, the underlining principles and basic functions of human vision and the eye have only been discovered during the last two centuries. These discoveries have led research in how to mimic these functions, which has resulted in moving and still-photographic and camera equipment, and the image sensors chips used in digital cameras today. Even though human beings are only taking baby steps in fully mimicking the human eye, curiosity and scientific inquiry allows us to discover functions and features of the eye and the visual pathways that will increase our knowledge and help us to build better pixels and image sensor chips.

References

1. Winer, G. A., Cottrell, J. E., Gregg, V., Fournier, J. S., & Bica, L. A., “Fundamentally misunderstanding visual perception: Adults’ beliefs in visual emissions.” American Psychologist, 57, 417-424, 2002.

2. Ertan Salik, “Pinhole Cameras, Imaging, and The Eye” The Fountain Magazine, Issue 54, pp. 30-33, April – June 2006.

3. URL: http://en.wikipedia.org/wiki/Image:Cone-response.png

4. URL: http://www.handprint.com/HP/WCL/color3.html

5. Stefan Winkler, Digital Video Quality – Vision Models and Metrics, John-Wiley & Sons, Ltd., 2005.

6. URL: http://www.clarkvision.com/imagedetail/eye-resolution.html

7. R.C. Gonzalez and R.E. Woods, Digital Image Processing, Addison-Wesley, 1993.

8. M. Schanz, et al., “A high-dynamic-range CMOS image sensor for automotive applications”, IEEE Journal of Solid-State Circuits, vol. 35, no. 7, pp.932-938, July 2000.

It is important to understand the advantages and disadvantages of perceiving visual information. Throughout history, written and visual information (shapes and pictures) have been used together to perceive, think and, communicate. The discovery of the camera in 1895 enabled pictures of objects to be stored permanently. Photographic techniques also accelerated the development of science and industry.

In 1895, Roentgen discovered x-rays and developed a method to show a person’s bones and internal organs. This gave visual information-based perception more importance. In the past, we tried to sense and understand the surrounding world by using the naked eye within a narrow band of energy originating from the sun. Later on, we realized that we could see only a fraction of what was there.

Virtual reality

Virtual reality environments make perceiving and learning easier and more effective. Events that are expensive and require a long time to perceive and learn can be perceived and learned with less effort and less cost. For example, a virtual reality environment enables a person to tour the ocean’s bottom and deep space within several minutes. In addition, it reveals formerly unseen micro- and macro-objects visible to the naked eye, just as if they were being seen in a dream or were real.

The most powerful visual information techniques are in the advertising, marketing, news, and entertainment sectors. The virtual reality environment is just one cutting-edge development. To gain self-control and learn effectively, an individual can be placed in such an environment, which fully replicates the real world. Such environments are used, for example, to train pilots and doctors. Another suitable use would be to train Muslims for Hajj and Umrah. Such an experience would familiarize them with Makka and Madina, thereby lessening the potential for unexpected problems and better prepare them intellectually and spiritually for these holy voyages.

Visual information and education

The techniques of making objects visible started with discovery of the microscope and the telescope. When combined with multimedia technology, they have an important place in modern education. The correct and effective use of visual information and written information-based perception is improving the quality of education. Similarly, popular Web pages have been transformed into marketing environments that convey their messages through the combined use of scripts, images, sounds, and animation.

Surrounded by these visual tools and techniques, the media has a greater influence upon society than ever before, for it controls the visual information. Educators should teach their students how to analyze and criticize visual information, for producing fake computer-produced images in a visual information-based environment is almost as easy as producing fake written information with a copy machines. They must learn not to believe everything that they see, just as their parents learned not to believe everything that they read in the newspapers.

Conclusion

During the last several centuries, humanity has been led to believe that seeing is believing and don’t believe in what you can’t see. However, advanced visual processing techniques prove that not everything that we see is correct. In fact, such techniques might actually misled individuals, for they can produce images or virtual reality environments very close to the real one.

Another feature of the human visual system is the ease with which interpretation is carried out. We see a scene as it is – trees in a landscape, books on a desk, products in a factory. No obvious deductions are needed and no overt effort is required to interpret each scene. In addition, answers are immediate and available normally within a tenth of a second. The important point is that we are for the most part unaware of the complexities of vision. Seeing is not a simple process.

We are still largely ignorant of the process of human vision. However, man is inventive and he is now trying to get machines to do much of his work for him. For the simplest tasks there should be no particular difficulty in mechanization but for more complex tasks the machine must be given man’s prime sense, i.e. that of vision. Efforts have been made to achieve this, sometimes in modest ways, for well over 30 years. At first, such tasks seemed trivial and schemes were devised for reading, for interpreting chromosome images and so on. But when such schemes were confronted with rigorous practical tests, the problems often turned out to be more difficult.

Computer vision blends optical processing and sensing, computer architecture, mechanics and a deep knowledge of process control. Despite some success, in many fields of application it really is still in its infancy.

With the current state of computing technology, only digital images can be processed by our machines. Because our computers currently work with numerical rather than pictorial data, an image must be converted into numerical form before processing.

The field of machine or computer vision may be sub-divided into six principal areas (1) sensing, (2) pre-processing, (3) segmentation, (4) description (5) recognition and (6) interpretation. Sensing is the process that yields a visual image. The sensor, most commonly a TV camera, acquires an image of the object that is to be recognised or inspected. The digitizer converts this image into an array of numbers, representing the brightness values of the image at a grid of points; the numbers in the array are called pixels. Pre-processing deals with techniques such as noise reduction and the enhancement of details. The pixel array is fed into the processor, a general-purpose or custom-built computer that analyses the data and makes the necessary decisions. Segmentation is the process that partitions an image into objects of interest. The segmentation of images should result in regions which correspond to objects, parts of objects or groups of objects which appear in the image. These features of these entities, along with their positions relative to the entire image, help us to make a meaningful interpretation. Description deals with the computation of features such as size, shape, texture, etc. suitably for differentiating one type of object from another. Recognition is the process that identifies these objects. Finally, interpretation assigns meaning to an ensemble of recognised objects.

Any description of the human visual system only serves to illustrate how far computer vision has to go before it approaches human ability.

In terms of image acquisition, the eye is totally superior to any camera system yet developed. The retina, on which the upside-down image is projected, contains two classes of discrete light receptors – cones and rods. There are between 6 and 7 million cones in the eye, most of them located in the central part of the retina called the fovea. These cones are highly sensitive to colour and the eye muscles rotate the eye so that the image is focused primarily on the fovea. The cones are also sensitive to bright light and do not operate in dim light. Each cone is connected by its own nerve to the brain.

There are at least 75 million rods in the eye distributed across the surface of the retina. They are sensitive to light intensity but not to colour.

The range of intensities to which the eye can adapt is of the order of 1010, from the lowest visible light to the highest bearable glare. In practice the eye per forms this amazing task by altering its own sensitivity depending on the ambient level of brightness.

Of course, one of the ways in which the human visual system gains over the machine is that the brain possesses some 1010 cells (or neurons), some of which have well over 10,000 contacts (or synapses) with other neurons. If each neuron acts as a type of microprocessor, then we have an immense computer in which all the processing elements can operate concurrently. Probably, the largest manmade computer still contains less than a million processing elements, so the majority of the visual and mental processing tasks that the eye-brain system can perform in a flash have no chance of being performed by present-day man-made systems.

Added to these problems of scale is the problem of how to organize such a large processing system, and also how to program it. Clearly, the eye- rain system is partly hard-wired but there is also an interesting capability to program it dynamically by training during active use. This need for a large parallel processing system with the attendant complex control problems illustrates clearly that machine vision must indeed be one of the most difficult intellectual problems to tackle.

Part of the problem lies in the fact that the sophistication of the human visual system makes robot vision systems pale by comparison.

Developing general-purpose computer vision systems has been proved surprisingly difficult and complex. This has been particularly frustrating for vision researchers, who experience daily the apparent ease and spontaneity of human perception.

As can be seen from the information given above, developing a computer-vision system requires knowledge. Hence the eye performs better and better than even the best computer-vision system, the very complex eye-brain system also requires more comprehensive knowledge to build. When man understands and discovers the functioning of the eye-brain system, better computer-vision systems will be developed. That means the eye-brain system (like other systems in the body of human being) is a very deep and rich knowledge source. As this system has links with other systems in the body, it obviously shows that the maker of these systems is One who knows everything about every single part of the whole body, for not only the eye-brain system but the entire body develops accordingly.

So, who can be the maker of this fantastic eye-brain system? If it is said that it is self-creating, this has no meaning because everybody knows that such an important system cannot create itself, just as a computer-vision system cannot give itself existence. As for chance, is it possible for such a system, which is full of knowledge for human being to imitate in order to develop computer- vision systems, to be made by chance at all? Of course not. So who is the maker of the eye-brain system?

The maker or the creator of this system can only be One who has supernatural power. He says in His Holy Book:

‘Have we not made for him (human being) a pair of eyes?’ (The Holy Qur’an 90.8).

Yes, indeed, He made them just as He made the whole of the rest of the cosmos with His unlimited knowledge.

REFERENCES

• DAVIES, B. R. (1990) Machine vision: theory, algorithms, practicalities, Academic Press, New York.
• GOWRISBANKAR, T. R, & BOURBAKIS, N. G. (June 1990) Specifications for the development of a knowledge-based image-interpretation systems, Engineering Applications of Al, Vol. 3, pp.79-9O.
• MAJUMDER, D. D. (1988) Computer vision and knowledge based computer systems, Institution of Electronics and Telecom. Engineers, Vol. 34, No 3, pp. 230-245.
• MULLER. B. & REINHARDT. J. (1990) Neural networks, an introduction. Springer- Verlag Publications, New York.
• RODD, M. G. (1990) Knowledge based vision systems, Knowledge Engineering, Vol. 2. ed, Adeli, II., McGraw [Jill, pp.245-276.
• ROSENFELD, A. (August 1985) Machine vision for industry: tasks, tools and techniques, Image and Vision Computing, Vol. 3, No 3. pp.122-135.
• SANDERSON, 1