Evaluation of dynamic programming among the existing stereo matching algorithms

There are various types of existing stereo matching algorithms on image processing which applied on stereo vision images to get better results of disparity depth map. One of them is the dynamic programming method. On this research is to perform an evaluation on the performance between the dynamic programming with other existing method as comparison. The algorithm used on the dynamic programming is the global optimization which provides better process on stereo images like its accuracy and its computational efficiency compared to other existing stereo matching algorithms. The dynamic programming algorithm used on this research is the current method as its disparity estimates at a particular pixel and all the other pixels unlike the old methods which with scanline based of dynamic programming. There will be details on every existing methods presented on this paper with the comparison between the dynamic programming and the existing methods. This can propose the dynamic programming method to be used on many applications in image processing

WILDetect - Part II

A new non-parametric approach, WILDetect, has been built using an ensemble of supervised Machine Learning (ML) and Reinforcement Learning (RL) techniques. Readers may recall that the first part of the paper was published in May' 24 issue of Coordinates magazine. We present here the concluding part. The habitats of marine life, characteristics of species, and the diverse mix of maritime industries around these habitats are of interest to many researchers, authorities, and policymakers whose aim is to conserve the earth’s biological diversity in an ecologically sustainable manner while being in line with indispensable industrial developments. Automated detection, locating, and monitoring of marine life along with the industry around the habitats of this ecosystem may be helpful to (i) reveal current impacts, (ii) model future possible ecological trends, and (iii) determine required policies which would lead accordingly to a reduced ecological footprint and increased sustainability. New automatic techniques are required to observe this large environment efficiently. Within this context, this study aims to develop a novel platform to monitor marine ecosystems and perform bio census in an automated manner, particularly for birds in regional aerial surveys since birds are a good indicator of overall ecological health. In this manner, a new non-parametric approach, WILDetect, has been built using an ensemble of supervised Machine Learning (ML) and Reinforcement Learning (RL) techniques. It employs several hybrid techniques to segment, split and count maritime species — in particular, birds — in order to perform automated censuses in a highly dynamic marine ecosystem. The efficacy of the proposed approach is demonstrated by experiments performed on 26 surveys which include Northern gannets (Morus bassanus) by utilising retrospective data analysis techniques. With this platform, by combining multiple techniques, gannets can be detected and split automatically with very high sensitivity (Se) (0.97), specificity (Sp) (0.99), and accuracy (Acc) (0.99) — these values are validated by precision (Pr) (0.98). Moreover, the evaluation of the system by the APEM staff, which uses a completely new evaluation dataset gathered from recent surveys, shows the viability of the proposed techniques. The experimental results suggest that similar automated data processing techniques — tailored for specific species — can be helpful both in performing time-intensive marine wildlife censuses efficiently and in establishing ecological platforms/models to understand the underlying causes of trends in species populations along with the ecological change

A Comparison of Image Processing Techniques for Bird Detection

Orchard fruits and vegetable crops are vulnerable to wild birds and animals. These wild birds and animals can cause critical damage to the produce. Traditional methods of scaring away birds such as scarecrows are not long-term solutions but short-term solutions. This is a huge problem especially near areas like San Luis Obispo where there are vineyards. Bird damage can be as high as 50% for grapes being grown in vineyards. The total estimated revenue lost annually in the 10 counties in California due to bird and rodent damage to 22 selected crops ranged from $168 million to$504 million (in 2009 dollars). A more effective and permanent system needs to be put into place. Monitoring systems in agricultural settings could potentially provide a lot of data for image processing. Most current monitoring systems however don’t focus on image processing but instead really heavily on sensors. Just having sensors for certain systems work, but for birds, monitoring it is not an option because they are not domesticated like pigs, cows etc. in which most these agricultural monitoring systems work on. Birds can fly in and out of the area whereas domesticated animals can be confined to certain physical regions. The most crucial step in a smart scarecrow system would be how a threat would v be detected. Image processing methods can be effectively applied to detecting items in video footage. This paper will focus on bird detection and will analyze motion detection with image subtraction, bird detection with template matching, and bird detection with the Viola-Jones Algorithm. Of the methods considered, bird detection with the Viola-Jones Algorithm had the highest accuracy (87%) with a somewhat low false positive rate. This image processing step would ideally be incorporated with hardware (such as a microcontroller or FPGA, sensors, a camera etc.) to form a smart scarecrow system

An efficient algorithm for exhaustive template matching based on normalized cross correlation

none3noThis work proposes a novel technique aimed at improving the performance of exhaustive template matching based on the normalized cross correlation (NCC). An effective sufficient condition, capable of rapidly pruning those match candidates that could not provide a better cross correlation score with respect to the current best candidate, can be obtained exploiting an upper bound of the NCC function. This upper bound relies on partial evaluation of the crosscorrelation and can be computed efficiently, yielding a significant reduction of operations compared to the NCC function and allows for reducing the overall number of operations required to carry out exhaustive searches. However, the bounded partial correlation (BPC) algorithm turns out to be significantly data dependent. In this paper we propose a novel algorithm that improves the overall performance of BPC thanks to the deployment of a more selective sufficient condition which allows for rendering the algorithm significantly less data dependent. Experimental results with real images and actual CPU time are reported. © 2003 IEEE.mixedDi Stefano L.; Mattoccia S.; Mola M.Di Stefano L.; Mattoccia S.; Mola M

Design of a High-Speed Architecture for Stabilization of Video Captured Under Non-Uniform Lighting Conditions

Video captured in shaky conditions may lead to vibrations. A robust algorithm to immobilize the video by compensating for the vibrations from physical settings of the camera is presented in this dissertation. A very high performance hardware architecture on Field Programmable Gate Array (FPGA) technology is also developed for the implementation of the stabilization system. Stabilization of video sequences captured under non-uniform lighting conditions begins with a nonlinear enhancement process. This improves the visibility of the scene captured from physical sensing devices which have limited dynamic range. This physical limitation causes the saturated region of the image to shadow out the rest of the scene. It is therefore desirable to bring back a more uniform scene which eliminates the shadows to a certain extent. Stabilization of video requires the estimation of global motion parameters. By obtaining reliable background motion, the video can be spatially transformed to the reference sequence thereby eliminating the unintended motion of the camera. A reflectance-illuminance model for video enhancement is used in this research work to improve the visibility and quality of the scene. With fast color space conversion, the computational complexity is reduced to a minimum. The basic video stabilization model is formulated and configured for hardware implementation. Such a model involves evaluation of reliable features for tracking, motion estimation, and affine transformation to map the display coordinates of a stabilized sequence. The multiplications, divisions and exponentiations are replaced by simple arithmetic and logic operations using improved log-domain computations in the hardware modules. On Xilinx\u27s Virtex II 2V8000-5 FPGA platform, the prototype system consumes 59% logic slices, 30% flip-flops, 34% lookup tables, 35% embedded RAMs and two ZBT frame buffers. The system is capable of rendering 180.9 million pixels per second (mpps) and consumes approximately 30.6 watts of power at 1.5 volts. With a 1024×1024 frame, the throughput is equivalent to 172 frames per second (fps). Future work will optimize the performance-resource trade-off to meet the specific needs of the applications. It further extends the model for extraction and tracking of moving objects as our model inherently encapsulates the attributes of spatial distortion and motion prediction to reduce complexity. With these parameters to narrow down the processing range, it is possible to achieve a minimum of 20 fps on desktop computers with Intel Core 2 Duo or Quad Core CPUs and 2GB DDR2 memory without a dedicated hardware

Computer vision for geometrical analysis of bridge gusset plates

Gusset plates are an important component of bridges. They are thick sheets of steel that join steel members together using fasteners and also strengthen their joint. Transportation agencies regularly evaluate and rate their inventories of gusset plate connections using visual inspection, which is very costly. To address this issue, we have developed a new interactive software tool, called Gusset Plate Inspection Tool (GPIT) that analyses images of gusset plates using computer vision. This tool is aimed at rapidly and accurately collecting field measurements from the images and analyzing the connection plate geometry interactively. To the best of our knowledge, this represents the first attempt at achieving accurate measurements directly from image pixels. GPIT consists of the following modules: Target detection and Perspective removal, Boundary marking, Fastener detection and grouping and Member Analysis. The result of each module acts as an input to the next. The final stage of image analysis involves a series of calculations. To address the requirement of precise output from these calculations, this tool enables human interaction. The GPIT allows two modes of analysis –automatic and manual. The former allows the user to batch process a set of images and the latter permits individual handling of a single image. GPIT has been evaluated in a user study by a set of students from both civil and computer engineering following the standard heuristics of qualitative evaluation of software systems. The response is overall positive. GPIT has been deployed and is in use by departments of transportation in Oregon, Arizona and Washington

Navegação baseada no terreno com dados de sonar de varrimento lateral

Mestrado em Engenharia Electrónica e TelecomunicaçõesO presente trabalho propõe uma técnica de matching de imagens de sonar de varrimento lateral, baseada nos conceitos de entropia de Shannon e informação mútua, com aplicação à navegação de veículos robóticos subaquáticos. Para tal, recorre-se não só à informação fornecida pela escala de cinzentos, como também à informação fornecida por um conjunto de features texturais (Haralick Features), extraídas dos dados de sonar. Uma parte significativa do trabalho incide sobre o estudo e a implementação dos algoritmos de estimação de entropia, para cálculo da informação mútua. É feita também uma contextualização do problema proposto, onde, para além da apresentação dos conceitos teóricos envolvidos, são definidos os objectivos gerais, assim como uma revisão às tecnologias de navegação subaquática existentes. Os métodos propostos são validados experimentalmente com dados obtidos de distribuições probabilísticas conhecidas e que permitem a validação dos mesmos analiticamente. São realizados testes adicionais com imagens fotográficas e com dados de SSS para validação daqueles métodos. Para a aplicação de navegação em vista, o cálculo da entropia das imagens baseia-se nos dados originais de SSS, representados em níveis de cinzento, complementados com a informação extraída desses dados segundo os métodos propostos originalmente por Haralick. A selecção das features texturais a usar é feita tendo em conta a natureza dos dados, as limitações associadas à aquisição dos mesmos em ambiente submarino e os objectivos da sua aplicação em termos de navegação. Para tal apresenta-se uma proposta de classificação e escolha das Haralick Features mais adequadas a imagens de sonar de varrimento lateral. Os testes finais aplicam a metodologia proposta a dados reais de sonar de varrimento lateral para demonstrar o potencial da sua utilização no âmbito da navegação de veículos robóticos subaquáticos.The the work presented here proposes a technique for matching of Sidescan Sonar images, based on Shannon’s Entropy and Mutual Information concepts, applied to the naviagtion of underwater robotic vehicles. For such, it is used not only the information given by the grey-scale levels, but also by a set of textural features (Haralick features), which are extracted from the sonar data. A significant parto f this work was spent studying and implementing algorithms for entropy estimation and mutual information calculation. The theoretical concepts involved are presented, the objectives are defined as well as a revision of underwater navigation technologies in existence. All this is done in order to contextualize the proposed problem. The proposed methods are validated experimentally through data acquired from known probability distributions which allow for na analytical validation.Aditional tests with both photographic images and sidescan sonar data are performed in order to validate such methods. For the intended navigational application, the images’ entropy estimation is based on the original sidescan sonar data. This data is represented by grey-scale levels complemented by the information extracted in accordance to the method proposed by Haralick. The selection of the textural features is done by taking into account the data’s nature, limitations associated with data acquisition in submarine environments and the objectives of its application in terms of navigation. It is therefore proposed a more adequate classification and selection of the Haralick features. The final tests are done by applying the proposed methodology to real sidescan sonar data in order to demonstrate its potential for use in assisting robotic underwater vehicles’ navigation