Multiple Objects In Image Research Articles

Development of Correspondence Algorithm for Illumination Varying Dynamic Image Sequence using Symbolic Data Analysis: A Novel Approach

This paper presents a novel symbolic data analysis based correspondence algorithm to identify the correspondence of illumination varying multiple object image sequence for the dynamic image analysis. The correspondence is the matching of the similar data sets or entities or the features such as the region based global features, points, corners, edges between a set of images. The Correspondence of illumination varying multispectral, temporal multiple objects using symbolic data analysis of objects in the dynamic images is a highly challenging problem. The determining Correspondence of features is a high dimensional NP-hard problem, because of their exhaustive search for the features in the sequence of image frames. The current algorithm works on the basis of successive frame difference based segmentation. The minimum boundary rectangle constructed for segmented moving object region. Mean of the rectangle is considered as one of the numeric feature. Next the sequence converted to binary sequence based on the threshold and 8-connectivity of perimeter is another non numeric feature. Finally establish the correspondence using canonical symbolic data analysis. This work proposes the rectangular correspondence window, generated dynamically which depends on the size and the number of objects in the current frame. Here we have considered mean of minimum boundary rectangle and perimeter of the segmented region as the features. The experiment is conducted with single object in the scene and multiple objects in the scene of illumination varying, noisy image sequence. This experiment is carried out on the binary, gray and RGB color dynamic image sequence. This correspondence algorithm is especially suitable for tracking in indoor images illuminated by unconstrained multiple light sources, objects in the varying illumination environment, noisy image sequence, video surveillance and slow moving dynamic image sequence for indoor scenes of the constant background.

Anti-Temporal-Aliasing Constraints for Image-Based Feature Tracking Applications With and Without Inertial Aiding

Image-aided navigation techniques can determine the navigation solution (position, velocity, and attitude) by observing a sequence of images from an optical sensor over time. This operation is based on tracking the location of stationary objects in multiple images, which requires solving the correspondence problem. This paper enhances previous research efforts to characterize the correspondence problem using fundamental optical principles and statistical temporal sampling theory by including a rigorous derivation of the Nyquist constraint in pixel space. This development results in a general temporal sampling constraint and reveals the essential connection between the deleterious effects of temporal aliasing and the ambiguities that plague the correspondence search problem. This temporal image sampling constraint is expressed as a function of the navigation trajectory for elementary camera motions. The predicted temporal sampling (also known as frame) rates are on the order of those needed for adaptive optics control systems and require very large bandwidths. The temporal image sampling constraint is then reevaluated by incorporating inertial measurements. The incorporation of inertial measurements is shown to reduce the required temporal sampling rate to practical levels, which evidences the fundamental synergy between image and inertial sensors for navigation and serves as the basis for a real-time adaptive antialiasing strategy.

Electric imaging through active electrolocation: implication for the analysis of complex scenes

The electric sense of mormyrids is often regarded as an adaptation to conditions unfavourable for vision and in these fish it has become the dominant sense for active orientation and communication tasks. With this sense, fish can detect and distinguish the electrical properties of the close environment, measure distance, perceive the 3-D shape of objects and discriminate objects according to distance or size and shape, irrespective of conductivity, thus showing a degree of abstraction regarding the interpretation of sensory stimuli. The physical properties of images projected on the sensory surface by the fish's own discharge reveal a "Mexican hat" opposing centre-surround profile. It is likely that computation of the image amplitude to slope ratio is used to measure distance, while peak width and slope give measures of shape and contrast. Modelling has been used to explore how the images of multiple objects superimpose in a complex manner. While electric images are by nature distributed, or 'blurred', behavioural strategies orienting sensory surfaces and the neural architecture of sensory processing networks both contribute to resolving potential ambiguities. Rostral amplification is produced by current funnelling in the head and chin appendage regions, where high density electroreceptor distributions constitute foveal regions. Central magnification of electroreceptive pathways from these regions particularly favours the detection of capacitive properties intrinsic to potential living prey. Swimming movements alter the amplitude and contrast of pre-receptor object-images but image modulation is normalised by central gain-control mechanisms that maintain excitatory and inhibitory balance, removing the contrast-ambiguity introduced by self-motion in much the same way that contrast gain-control is achieved in vision.

A Theory Of Frequency Domain Invariants: Spherical Harmonic Identities for BRDF/Lighting Transfer and Image Consistency

This paper develops a theory of frequency domain invariants in computer vision. We derive novel identities using spherical harmonics, which are the angular frequency domain analog to common spatial domain invariants such as reflectance ratios. These invariants are derived from the spherical harmonic convolution framework for reflection from a curved surface. Our identities apply in a number of canonical cases, including single and multiple images of objects under the same and different lighting conditions. One important case we consider is two different glossy objects in two different lighting environments. For this case, we derive a novel identity, independent of the specific lighting configurations or BRDFs, that allows us to directly estimate the fourth image if the other three are available. The identity can also be used as an invariant to detecttampering in the images. While this paper is primarily theoretical, it has the potential to lay the mathematical foundations for two important practical applications. First, we can develop more general algorithms for inverse rendering problems, which can directly relight and change material properties by transferring the BRDF or lighting from another object or illumination. Second, we can check the consistency of an image, to detect tampering or image splicing.

Tracking of Multiple Moving Objects in Dynamic Image of Omni-Directional Camera Using PHD Filter

A method of multiple moving objects tracking in dynamic image of omni-directional camera has been proposed. Finite random set (FRS) based state space model is employed in the method due to its inherent nature capable to represent the scene having occlusion and appearance of object as well as missing and false detection in observation. Sequential Monte Carlo (SMC) implementation of Probability hypothesis density (PHD) filter has been used for estimating state of the state space model. The state is a finite random set of single object states, where each element of the set consists of position and velocity of the object in panoramic image coordinate of omni-directional camera image. We propose a new method to display tracking result from weighted particles obtained from the estimation process by SMC implementation of PHD filter. Key idea of the method is to put an integer label on each particle, where the label indicates specific object among multiple objects in the image scene tracked by the particle. Numerical simulation and real image experiments illustrate tracking performance of the proposed method.

Automated inspection of PCB components using a genetic algorithm template-matching approach

Automated inspection of surface mount PCB boards is a requirement to assure quality and to reduce manufacturing scrap costs and rework. This paper investigates methodologies for locating and identifying multiple objects in images used for surface mount device inspection. One of the main challenges for surface mount device inspection is component placement inspection. Component placement errors such as missing, misaligned or incorrectly rotated components are a major cause of defects and need to be detected before and after the solder reflow process. This paper focuses on automated object-recognition techniques for locating multiple objects using grey-model fitting for producing a generalised template for a set of components. The work uses the normalised cross correlation (NCC) template-matching approach and examines a method for constraining the search space to reduce computational calculations. The search for template positions has been performed exhaustively and by using a genetic algorithm. Experimental results using a typical PCB image are reported.

Stochastic constraints for efficient image correspondence search

The navigation state (position, velocity, and attitude) can be determined using optical measurements from an imaging sensor pointed toward the ground. Extracting navigation information from an image sequence depends on tracking the location of stationary objects in multiple images, which is generally termed the correspondence problem. This is an active area of research and many algorithms exist which attempt to solve this problem by identifying a unique feature in one image and then searching subsequent images for a feature match. In general, the correspondence problem is plagued by feature ambiguity, temporal feature changes, and occlusions which are difficult for a computer to address. Constraining the correspondence search to a subset of the image plane has the dual advantage of increasing robustness by limiting false matches and improving search speed. A number of ad hoc methods to constrain the correspondence search have been proposed in the literature. A rigorous stochastic projection method is developed here which constrains the correspondence search space by incorporating a priori knowledge of the aircraft navigation state using inertial measurements and a statistical terrain model. The stochastic projection algorithm is verified using Monte Carlo simulation and flight data. The constrained correspondence search area is shown to accurately predict the pixel location of a feature with an arbitrary level of confidence, thus promising improved speed and robustness of conventional algorithms

Matching pursuit‐based shape representation and recognition using scale‐space

AbstractIn this paper, we propose an analytical low‐level representation of images, obtained by a decomposition process, namely the matching pursuit (MP) algorithm, as a new way of describing objects through a general continuous description using an affine invariant dictionary of basis function (BFs). This description is used to recognize multiple objects in images. In the learning phase, a template object is decomposed, and the extracted subset of BFs, called meta‐atom, gives the description of the object. This description is then naturally extended into the linear scale‐space using the definition of our BFs, and thus providing a more general representation of the object. We use this enhanced description as a predefined dictionary of the object to conduct an MP‐based shape recognition task into the linear scale‐space. The introduction of the scale‐space approach improves the robustness of our method: we avoid local minima issues encountered when minimizing a nonconvex energy function. We show results for the detection of complex synthetic shapes, as well as real world (aerial and medical) images. © 2007 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 16, 162–180, 2006

Greedy learning of multiple objects in images using robust statistics and factorial learning.

We consider data that are images containing views of multiple objects. Our task is to learn about each of the objects present in the images. This task can be approached as a factorial learning problem, where each image must be explained by instantiating a model for each of the objects present with the correct instantiation parameters. A major problem with learning a factorial model is that as the number of objects increases, there is a combinatorial explosion of the number of configurations that need to be considered. We develop a method to extract object models sequentially from the data by making use of a robust statistical method, thus avoiding the combinatorial explosion, and present results showing successful extraction of objects from real images.

Fractscript: A macro for calculating the fractal dimension of object perimeters in images of multiple objects

Fractscript is a macro that uses the box-counting technique to calculate the fractal dimension of multiple objects within an individual image. This paper describes Fractscript and verifies its results using objects with known fractal dimension. The concept of fractal dimensions aids in the understanding and partial ordering of apparently random geological forms. By reducing user input and standardising any approach to the calculation of the fractal dimension, the automation of techniques that can be used to calculate the fractal dimension of geological material may prove to be both expedient and accurate. The macro was also tested successfully on images from thin sections of sandstones, focusing on individual porel outlines, the results of which are presented as examples.

Appearance-based visual learning and object recognition with illumination invariance

This paper describes a method for recognizing partially occluded objects under different levels of illumination brightness by using the eigenspace analysis. In our previous work, we developed the “eigenwindow” method to recognize the partially occluded objects in an assembly task, and demonstrated with sufficient high performance for the industrial use that the method works successfully for multiple objects with specularity under constant illumination. In this paper, we modify the eigenwindow method for recognizing objects under different illumination conditions, as is sometimes the case in manufacturing environments, by using additional color information. In the proposed method, a measured color in the RGB color space is transformed into one in the HSV color space. Then, the hue of the measured color, which is invariant to change in illumination brightness and direction, is used for recognizing multiple objects under different illumination conditions. The proposed method was applied to real images of multiple objects under various illumination conditions, and the objects were recognized and localized successfully.

Auditory perception of objects by blind persons, using a bioacoustic high resolution air sonar.

A high-resolution octave band air sonar for spatial sensing and object imaging by blind persons is described. The system has wide-angle overlapping peripheral fields of view with a narrow central field superposed. It is noninvasively coupled to the auditory system for neural processing and spatial imaging. Blind persons learn to comprehend the auditory cortical multiple-object image that is created. The real-time synchronous relationship between hearing a change in the sensor sounds and the sensed motor actions causing the change seems to aid the learning process. Computer based testing of the sensor system is described so as to relate the physical system performance with the time-varying human auditory perception. This is so that the basic psychometric experiments studying the sensor bio-acoustic spatial resolution, resulting from the superposition of two wide-angle peripheral fields with one central narrow field, may be better understood. These tests confirm that the auditory ability of subjects to resolve close objects using the combined fields is significantly improved relative to using the peripheral fields alone. These measurements are supported by blind children learning to use the sensor system.

Tomographic time-of-flight optical imaging device.

Time-resolved optical imaging has been used to image phantoms, animals, and humans, and offers the potential for the production of functional images of human tissues, such as the oxygenation of brain during stroke. We had previously reported a transmission scanner, and now give an early report on conversion to a rotational tomographic scanner with a non-parallel ray geometry similar to early CAT scanners. Initial scans show that 1) spatial imaging in turbid media using time-of-flight measurements, non-recursive algorithms, and standard tomographic geometry is possible, 2) separation of absorbance and scattering as an image is attainable, a key step in performing spatially-resolved chemometric analysis, 3) imaging of multiple objects buried within scattering material is feasible, demonstrating that equations derived for homogeneous media can be applied in at least some cases to inhomogeneous media such as tissue-like phantoms, and 4) imaging of brain pathology produces recognizable images with sufficient resolution for diagnostic decisions. We conclude that optical tomography is feasible for clinical use and that conversion of the present mechanically scanning device to a clinical scanner should be possible with retention of the current processing algorithms. Such a clinical scanner should ultimately be able to generate images in a few minutes with centimeter resolution at the center of living human brain.

A possible neuronal basis for representation of acoustic scenes in auditory cortex of the big brown bat.

Behavioural studies and field observations demonstrate that echolocating bats simultaneously perceive range, direction and shape of multiple objects in the environment as acoustic images derived from echoes. Cortical echo delay-tuned neurons contribute to the perception of object range, because focal inactivation of these neurons disrupts behavioural discrimination of range. We report here that response properties of delay-tuned neurons in the cortical tonotopic area of the bat, Eptesicus, transform the sequential arrival times of echoes with different delays into a concurrent, accumulating neural representation of multiple objects at different ranges. The sharpness of delay tuning systematically increases at each best delay in a subpopulation of these neurons while responses to echoes at different delays are accumulated. The resulting concurrent, multiresolution representation of echo delay corresponds to neural implementation of a common representation of images used in computational vision and may provide the basis for representing acoustic images of multiple objects as acoustic 'scenes'.

Three-dimensional shape construction and recognition by fusing intensity and structured lighting

In this paper, a new technique is developed for three-dimensional (3D) shape reconstruction and recognition from two-dimensional (2D) images of multiple objects with mutual occlusion. To facilitate such an analysis, both intensity and structured-light coded images are used. From intensity images, equi-brightness contours, which are swaths of points sharing similar intensities, are located. It is shown that equi-brightness contours possess distinct shape and orientation for different types of surfaces, and hence, can be used to distinguish them. To achieve a quantitative description of the shape and orientation of the imaged surfaces, the projected pattern in the corresponding structured-light coded images is analyzed. A scheme is developed which relates the orientation and curvature of the projected pattern observed in the structured-light images to the surface orientation and principal surface curvatures of the imaged objects.

A dynamically reconfigurable multiprocessor system for high-order bidirectional associative memory-based image recognition

A High-Order Bidirectional Associative Memory (HOBAM) based image recognition system and a dynamically reconfigurable multiprocessor system that achieves real-time response have been utilized to recognize corrupt images of human faces (faces obscured by hats, glasses, masks or slight translation and scaling effects). In addition, the HOBAM, in conjunction with edge detection techniques, has been used to recognize isolated objects within multiple-object images. Successful recognition rates have been achieved in both cases. A dynamically reconfigurable multiprocessor system and parallel software have been developed to achieve real-time response for image recognition. The system consists of Inmos transputers and cross-bar switches (IMS C004) with communication links dynamically connected by circuit switching. The use of transputers and cross-bar switches combined to form a low-cost multiprocessor system connected by a switching network is reported. Moreover, the switching network, which makes message routing unnecessary, simplifies the design of the communication in parallel software. Although the HOBAM is a fully connected network, the algorithm minimizes the amount of information that needs to be exchanged between processors using a data compression technique. The detailed design of both hardware and software are discussed, as well as the use of parallel processing to significantly increase the speed ratio. The architecture of the experimental system is a cost-effective design for an embedded system for neural network applications on computer vision.

Imaging with photographic diffusers

A photographically made amplitude diffuser has been shown to produce in its close vicinity a system of multiple images of objects placed in front of it at a certain distance. A qualitative explanation of the formation of such images has been given in terms of pinhole imaging and boundary diffraction wave theory. A report on several interesting features of the imaging with photographic diffusers is presented.