3D Camouflaging Object using RGB-D Sensors

This paper proposes a new optical camouflage system that uses RGB-D cameras, for acquiring point cloud of background scene, and tracking observers eyes. This system enables a user to conceal an object located behind a display that surrounded by 3D objects. If we considered here the tracked point of observer s eyes is a light source, the system will work on estimating shadow shape of the display device that falls on the objects in background. The system uses the 3d observer s eyes and the locations of display corners to predict their shadow points which have nearest neighbors in the constructed point cloud of background scene.Comment: 6 pages, 12 figures, 2017 IEEE International Conference on SM

Terrain Specific Real Time Pixelated Camouflage Texture Generation & its Impact Assessment

“Camouflage” is a natural or nature identical phenomenon where the sensory route of vision is delayed toavoid visual detection. Reducing detection capability and hiding in the background environment is critical for Army vehicles, equipment, and soldiers. This research aims to implement a process that will generate digital camouflage patterns specific to the terrain. The adapted digital pattern helps an object blend symmetrically into the background environment. Pixelated textures combine macro and micro designs that blend with ambient shrubs, trees, branches, and shadows. The technique presented in this paper consists of the following main modules: terrain classification model, pixelated camouflage texture generation, and texture evaluation. Experiments have been conducted to detect camouflage objects in the scene to evaluate the performance of the resultant camouflage texture generated for a natural environment. Photo simulation and saliency maps for hidden object detection have been used to evaluate the effectiveness of generated textures for a given terrai

Recognition by directed attention to recursively partitioned images

A learning/recognition model (and instantiating program) is described which recursively combines the learning paradigms of conceptual clustering (Michalski, 1980) and learning-from-examples to resolve the ambiguities of real-world recognition. The model is based on neuropsychological and psychological evidence that the visual system is analytic, hierarchical, and composed of a parallel/serial dichotomy (many, see conclusions by Crick, 1984). Emulating the experimental evidence, parallel processes in the model decompose the image into components and cluster the constituents in much the same way as the image processing technique known as moment analysis (Alt, 1962). Serial, attentive mechanisms then reassemble the decompositions by investigating spatial relationships between components. The use of attentive mechanisms extends the moment analysis technique to handle alterations in structure and solves the contention problem created by combining the two learning paradigms. The contention results from a disagreement between the teacher and the model on what constitutes the salient features at the highest level of the symbol. There are four cases ZBT must handle, two of which result from the disagreement with the teacher. The parallel/serial dichotomy represents a vertical/horizontal tradeoff between the invariant and variant features of a domain. The resultant learned hierarchy allows ZBT to recognize structural differences while avoiding problems of exponential growth

Multi camera soccer player tracking

Now a day’s spread of super computers, existing of high resolution and low-priced video cameras, and increasing the computerized video analysis has made more curiosity in tracking algorithms. Automatic identification and tracing of multiple moving objects through video scene is an interesting field of computer visualization. Identification and tracking of multiple people is a vital and challenging task for many applications like human-computer interface, video communication, security application and surveillance system. Various researchers offer various algorithms but none of this was work properly to distinguish the players automatically when creating occlusion. The first step to tracking multiple objects in video sequence is detection. Background subtraction is a very popular and effective method for foreground detection (assuming that background should be stationary). In this thesis we apply various background subtraction methods to tackle the difficulties like changing illumination condition, background clutter and camouflage. The method we propose to overcome this problem is operates the background subtraction by calculating the Mahalanobis distances. The second step to track multiple moving objects in soccer scene by using particle filters method that estimate the non-Gaussian, non-linear state-space model, which is a multi-target tracking method. These methods are applied on real soccer video sequences and the result show that it is successfully track and distinguish the players. After tracking is done by using multi camera views, we collecting the data from all cameras and creating geometrical relationship between cameras called Homography

Occlusion handling in multiple people tracking

Object tracking with occlusion handling is a challenging problem in automated video surveillance. Occlusion handling and tracking have always been considered as separate modules. We have proposed an automated video surveillance system, which automatically detects occlusions and perform occlusion handling, while the tracker continues to track resulting separated objects. A new approach based on sub-blobbing is presented for tracking objects accurately and steadily, when the target encounters occlusion in video sequences. We have used a feature-based framework for tracking, which involves feature extraction and feature matching

BTLD+:A BAYESIAN APPROACH TO TRACKING LEARNING DETECTION BY PARTS

The contribution proposed in this thesis focuses on this particular instance of the visual tracking problem, referred as Adaptive Ap- iv \ufffcpearance Tracking. We proposed different approaches based on the Tracking Learning Detection (TLD) decomposition proposed in [55]. TLD decomposes visual tracking into three components, namely the tracker, the learner and detector. The tracker and the detector are two competitive processes for target localization based on comple- mentary sources of informations. The former searches for local fea- tures between consecutive frames in order to localize the target; the latter exploits an on-line appearance model to detect confident hy- pothesis over the entire image. The learner selects the final solution among the provided hypothesis. It updates the target appearance model, if necessary, reinitialize the tracker and bootstraps the detec- tor\u2019s appearance model. In particular, we investigated different ap- proaches to enforce the TLD stability. First, we replaced the tracker component with a novel one based on mcmc particle filtering; after- wards, we proposed a robust appearance modeling component able to characterize deformable objects in static images; after all, we inte- grated a modeling component able to integrate local visual features learning into the whole approach, lying to a couple layered represen- tation of the target appearance

Graphic design + biomimicry

GRAPHIC DESIGN + BIOMIMICRY: Integrating Nature into Modern Design Practices is a thesis that explores how to effectively integrate the methodologies and principles of graphic design and biomimicry. The objective is to create an innovative design process resulting in successful, sustainable and timeless design solutions. This process is meant to remind designers of the benefits nature has to offer in helping us solve many of the problems that society is currently grappling with today. Nature over 3.8 billion years has already used its imaginative prowess to find what works, what is appropriate, and most importantly, what lasts here on Earth. The final print application acts as a resource guidebook cataloging all of the research, processes, and findings throughout the documentation of this thesis. This includes the indirect method; applying nature\u27s fourteen design principles with the fourteen universal design principles and elements, as well as the direct method of the biomimetic design process; applying the six stages: (1) Defining, (2) Analyzing, (3) Observing, (4) Selecting, (5) Implementing, and (6) Evaluating. Each chapter within the resource guidebook is defined by each stage in the graphic design + biomimicry process. Informational charts, diagrams, text and photographs are also included throughout to enhance user comprehension of the subject matter that is presented. Overall, this thesis is meant to encourage designers to think differently, forcing themselves to innovate, experiment, push and adapt their designs further than ever before. The objective at hand is to create good design that also has the potential to do good, for the world and everything that encompasses it. We are on the cusp of great change: will designers curl up at the thought of this or embrace this new mode of thinking and biomimetic mindset to help shape a positive future for design, people, and most importantly, our planet

A biologically inspired optical flow system for motion detection and object identification

The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file.Title from title screen of research.pdf file (viewed on April 7, 2008)Includes bibliographical references.Thesis (M.S.) University of Missouri-Columbia 2007.Dissertations, Academic -- University of Missouri--Columbia -- Electrical and computer engineering.Optical flow is possibly the best known method for motion segmentation. However its application is restricted to offline processing as it requires extensive computational resources and time. This thesis explores an optical flow method derived from observation on vision system of diptereous insect. The proposed method , Biological Optical flow (BioOF) was implemented using series of first order filters, and, therefore is much faster than any existing machine coded optical flow algorithm beside being hardware implement able. Like other optical flow methods, the output of proposed BioOF has two components: horizontal optical flow and vertical optical flow; both of them can be combined in order to get a better final result in terms of motion segmentation. Unfortunately, this combined output of the BioOF can be heavily coupled with noise. So, in order to remove the noise, intensive image processing had to be performed. The result was an algorithm that can provide a good contour of the segmented object in an image. Finally the object contour is converted to a Fourier feature space leading to a representation that is rotational and translational invariant. Over this feature space various classification algorithms including SVM, feature subset forward selection, Scatter matrix, and a simple linear classifier using principal component analysis and Mahanabolis distance were investigated

Human motion analysis

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