This paper presents a method for extracting distinctive invariant features from images that can be used to perform reliable matching between different views of an object or scene. The features are invariant to image scale and rotation, and are shown to provide robust matching across a substantial range of affine distortion, change in 3D viewpoint, addition of noise, and change in illumination. The features are highly distinctive, in the sense that a single feature can be correctly matched with high probability against a large database of features from many images. This paper also describes an approach to using these features for object recognition. The recognition proceeds by matching individual features to a database of features from known objects using a fast nearest-neighbor algorithm, followed by a Hough transform to identify clusters belonging to a single object, and finally performing verification through least-squares solution for consistent pose parameters. This approach to recognition can robustly identify objects among clutter and occlusion while achieving near real-time performance.

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Distinctive Image Features from Scale-Invariant Keypoints

抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

An object recognition system has been developed that uses a new class of local image features. The features are invariant to image scaling, translation, and rotation, and partially invariant to illumination changes and affine or 3D projection. These features share similar properties with neurons in inferior temporal cortex that are used for object recognition in primate vision. Features are efficiently detected through a staged filtering approach that identifies stable points in scale space. Image keys are created that allow for local geometric deformations by representing blurred image gradients in multiple orientation planes and at multiple scales. The keys are used as input to a nearest neighbor indexing method that identifies candidate object matches. Final verification of each match is achieved by finding a low residual least squares solution for the unknown model parameters. Experimental results show that robust object recognition can be achieved in cluttered partially occluded images with a computation time of under 2 seconds.

/pdf/object-recognition-from-local-scale-invariant-features-5887b1cgwf.pdf

Object recognition from local scale-invariant features

The Scale-Invariant Feature Transform (or SIFT) algorithm is a highly robust method to extract and consequently match distinctive invariant features from images. These features can then be used to reliably match objects in diering images. The algorithm was rst proposed by Lowe [12] and further developed to increase performance resulting in the classic paper [13] that served as foundation for SIFT which has played an important role in robotic and machine vision in the past decade.

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

https://www.cell.com/neuron/pdf/S0896-6273(00)80832-6.pdf

Differential Processing of Objects under Various Viewing Conditions in the Human Lateral Occipital Complex

THE visual recognition of three-dimensional (3-D) objects on the basis of their shape poses at least two difficult problems. First, there is the problem of variable illumination, which can be addressed by working with relatively stable features such as intensity edges rather than the raw intensity images1,2. Second, there is the problem of the initially unknown pose of the object relative to the viewer. In one approach to this problem, a hypothesis is first made about the viewpoint, then the appearance of a model object from such a viewpoint is computed and compared with the actual image3–7. Such recognition schemes generally employ 3-D models of objects, but the automatic learning of 3-D models is itself a difficult problem8,9. To address this problem in computational vision, we have developed a scheme, based on the theory of approximation of multivariate functions, that learns from a small set of perspective views a function mapping any viewpoint to a standard view. A network equivalent to this scheme will thus 'recognize' the object on which it was trained from any viewpoint.

A network that learns to recognize three-dimensional objects.

Does the human brain represent objects for recognition by storing a series of two-dimensional snapshots, or are the object models, in some sense, three-dimensional analogs of the objects they represent? One way to address this question is to explore the ability of the human visual system to generalize recognition from familiar to unfamiliar views of three-dimensional objects. Three recently proposed theories of object recognition--viewpoint normalization or alignment of three-dimensional models [Ullman, S. (1989) Cognition 32, 193-254], linear combination of two-dimensional views [Ullman, S. & Basri, R. (1990) Recognition by Linear Combinations of Models (Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge), A. I. Memo No. 1152], and view approximation [Poggio, T. & Edelman, S. (1990) Nature (London) 343, 263-266]--predict different patterns of generalization to unfamiliar views. We have exploited the conflicting predictions to test the three theories directly in a psychophysical experiment involving computer-generated three-dimensional objects. Our results suggest that the human visual system is better described as recognizing these objects by two-dimensional view interpolation than by alignment or other methods that rely on object-centered three-dimensional models.

https://www.pnas.org/content/pnas/89/1/60.full.pdf

Psychophysical support for a two-dimensional view interpolation theory of object recognition

In many different spatial discrimination tasks, the human visual system exhibits hyperacuity-level performance by evaluating spatial relations with the precision of a fraction of a photoreceptor''s diameter. We propose that this impressive performance depends in part on a fast learning process that uses relatively few examples and occurs at an early processing stage in the visual pathway. We demonstrate that it is possible to synthesize from a small number of examples a simple (HyperBF) network that attains the required performance level. We verify with psychophysical experiments some key predictions of our conjecture. We show that fast stimulus-specific learning indeed takes place in the human visual system and that this learning does not transfer between two slightly different hyperacuity tasks.

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Fast Perceptual Learning in Visual Hyperacuity

r r Abstract: Functional magnetic resonance imaging was used in combined functional selectivity and retinotopic mapping tests to reveal object-related visual areas in the human occpital lobe. Subjects were tested with right, left, up, or down hemivisual field stimuli which were composed of images of natural objects (faces, animals, man-made objects) or highly scrambled (1,024 elements) versions of the same images. In a similar fashion, the horizontal and vertical meridians were mapped to define the borders of these areas. Concurrently, the same cortical sites were tested for their sensitivity to image-scrambling by varying the number of scrambled picture fragments (from 16-1,024) while controlling for the Fourier power spectrum of the pictures and their order of presentation. Our results reveal a stagewise decrease in

/pdf/a-sequence-of-object-processing-stages-revealed-by-fmri-in-1dz24wh4dy.pdf

Shimon Edelman

Papers

Differential Processing of Objects under Various Viewing Conditions in the Human Lateral Occipital Complex

A network that learns to recognize three-dimensional objects.

Psychophysical support for a two-dimensional view interpolation theory of object recognition

Fast Perceptual Learning in Visual Hyperacuity

A sequence of object-processing stages revealed by fMRI in the human occipital lobe.