Qualitative and quantitative analysis of these cross-sectional image data will be beneficial to the evaluation and the treatment of coronary artery diseases such as atherosclerosis. In all the phases preoperative, intraoperative, and postoperative during the transcatheter intervention procedure, computer vision techniques e.
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This provides beneficial guidance for the clinicians in surgical planning, disease diagnosis, and treatment assessment. In this paper, we present a systematical review on these state-of-the-art methods. We aim to give a comprehensive overview for researchers in the area of computer vision on the subject of transcatheter intervention. Research in medical computing is multi-disciplinary due to its nature, and hence, it is important to understand the application domain, clinical background, and imaging modality, so that methods and quantitative measurements derived from analyzing the imaging data are appropriate and meaningful.
We thus provide an overview on the background information of the transcatheter intervention procedures, as well as a review of the computer vision techniques and methodologies applied in this area. A programmable computational image sensor for high-speed vision. In this paper we present a programmable computational image sensor for high-speed vision.
This computational image sensor contains four main blocks: an image pixel array, a massively parallel processing element PE array, a row processor RP array and a RISC core.
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The pixel-parallel PE is responsible for transferring, storing and processing image raw data in a SIMD fashion with its own programming language. The PE array and RP array can finish great amount of computation with few instruction cycles and therefore satisfy the low- and middle-level high-speed image processing requirement. The RISC core controls the whole system operation and finishes some high-level image processing algorithms.
We utilize a simplified AHB bus as the system bus to connect our major components.
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Programming language and corresponding tool chain for this computational image sensor are also developed. Computer vision based nacre thickness measurement of Tahitian pearls. To maintain its excellent reputation on the international market, an obligatory quality control for every pearl deemed for exportation has been established by the local government.
One of the controlled quality parameters is the pearls nacre thickness. The evaluation is currently done manually by experts that are visually analyzing X-ray images of the pearls. In this article, a computer vision based approach to automate this procedure is presented. Even though computer vision based approaches for pearl nacre thickness measurement exist in the literature, the very specific features of the Tahitian pearl, namely the large shape variety and the occurrence of cavities, have so far not been considered.
The presented work closes the. Our method consists of segmenting the pearl from X-ray images with a model-based approach, segmenting the pearls nucleus with an own developed heuristic circle detection and segmenting possible cavities with region growing. Out of the obtained boundaries, the 2-dimensional nacre thickness profile can be calculated. A certainty measurement to consider imaging and segmentation imprecisions is included in the procedure. The proposed algorithms are tested on manually evaluated Tahitian pearls, showing that it is generally possible to automatically evaluate the nacre thickness of Tahitian pearls with computer vision.
Furthermore the results show that the automatic measurement is more precise and faster than the manual one.
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Investigation of safety analysis methods using computer vision techniques. This work investigates safety analysis methods using computer vision techniques. The vision -based tracking system is developed to provide the trajectory of road users including vehicles and pedestrians. Safety analysis methods are developed to estimate time to collision TTC and postencroachment time PET that are two important safety measurements.
Corresponding algorithms are presented and their advantages and drawbacks are shown through their success in capturing the conflict events in real time. The performance of the tracking system is evaluated first, and probability density estimation of TTC and PET are shown for 1-h monitoring of a Las Vegas intersection. Finally, an idea of an intersection safety map is introduced, and TTC values of two different intersections are estimated for 1 day from a.
Computer vision applications for coronagraphic optical alignment and image processing. Modern coronagraphic systems require very precise alignment between optical components and can benefit greatly from automated image processing. We discuss three techniques commonly employed in the fields of computer vision and image analysis as applied to the Gemini Planet Imager, a new facility instrument for the Gemini South Observatory. We describe how feature extraction and clustering methods can be used to aid in automated system alignment tasks, and also present a search algorithm for finding regular features in science images used for calibration and data processing.
Along with discussions of each technique, we present our specific implementation and show results of each one in operation. ArduEye vision chip on Stonyman breakout board connected to Arduino Mega 8 left and the Stonyman vision chips There is a significant need for small, light , less power-hungry sensors and sensory data processing algorithms in order to control the.
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Computer vision for microscopy diagnosis of malaria. This paper reviews computer vision and image analysis studies aiming at automated diagnosis or screening of malaria infection in microscope images of thin blood film smears. Existing works interpret the diagnosis problem differently or propose partial solutions to the problem. A critique of these works is furnished.
In addition, a general pattern recognition framework to perform diagnosis, which includes image acquisition, pre-processing, segmentation, and pattern classification components, is described. The open problems are addressed and a perspective of the future work for realization of automated microscopy diagnosis of malaria is provided. Deep hierarchies in the primate visual cortex: what can we learn for computer vision? Computational modeling of the primate visual system yields insights of potential relevance to some of the challenges that computer vision is facing, such as object recognition and categorization, motion detection and activity recognition, or vision -based navigation and manipulation.
This paper reviews some functional principles and structures that are generally thought to underlie the primate visual cortex, and attempts to extract biological principles that could further advance computer vision research. Organized for a computer vision audience, we present functional principles of the processing hierarchies present in the primate visual system considering recent discoveries in neurophysiology. The hierarchical processing in the primate visual system is characterized by a sequence of different levels of processing on the order of 10 that constitute a deep hierarchy in contrast to the flat vision architectures predominantly used in today's mainstream computer vision.
We hope that the functional description of the deep hierarchies realized in the primate visual system provides valuable insights for the design of computer vision algorithms , fostering increasingly productive interaction between biological and computer vision research. Associative Algorithms for Computational Creativity. Computational creativity, the generation of new, unimagined ideas or artifacts by a machine that are deemed creative by people, can be applied in the culinary domain to create novel and flavorful dishes. In fact, we have done so successfully using a combinatorial algorithm for recipe generation combined with statistical models for recipe ranking….
Hypertext-based computer vision teaching packages. Many applications have been developed on such http servers. At Cardiff we have developed a http hypertext based multimedia server, the Cardiff Information Server, using the widely available Mosaic system. The server provides a variety of information ranging from the provision of teaching modules, on- line documentation, timetables for departmental activities to more light hearted hobby interests.
One important and novel development to the server has been the development of courseware facilities. This ranges from the provision of on-line lecture notes, exercises and their solutions to more interactive teaching packages.
This paper will address the issues of the implementation of the Computer Vision and Image Processing packages, the advantages gained from using a hypertext based system and also will relate practical experiences of using the packages in a class environment. The paper addresses issues of how best to provide information in such a hypertext based system and how interactive image processing packages can be developed and integrated into courseware. The suite of tools developed facilitates a flexible and powerful courseware package that has proved popular in the classroom and over the Internet.
The paper will also detail many future developments we see possible. One of the key points raised in the paper is that Mosaic's hypertext language html is extremely powerful and yet relatively straightforward to use.
It is also possible to link in Unix calls so that programs and shells can be executed. This provides a powerful suite of utilities that can be exploited to develop many packages. After a brief review of OpenCL 1. As a case in point, we discuss, in some detail, popular object recognition algorithms part-based models , emphasizing the interplay and concurrent collaboration between the GPU and CPU. Can computational goals inform theories of vision?
One of the most lasting contributions of Marr's posthumous book is his articulation of the different "levels of analysis" that are needed to understand vision. Although a variety of work has examined how these different levels are related, there is comparatively little examination of the assumptions on which his proposed levels rest, or the plausibility of the approach Marr articulated given those assumptions.
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Marr placed particular significance on computational level theory, which specifies the "goal" of a computation , its appropriateness for solving a particular problem, and the logic by which it can be carried out. The structure of computational level theory is inherently teleological: What the brain does is described in terms of its purpose. I argue that computational level theory, and the reverse-engineering approach it inspires, requires understanding the historical trajectory that gave rise to functional capacities that can be meaningfully attributed with some sense of purpose or goal, that is, a reconstruction of the fitness function on which natural selection acted in shaping our visual abilities.
I argue that this reconstruction is required to distinguish abilities shaped by natural selection-"natural tasks" -from evolutionary "by-products" spandrels, co-optations, and exaptations , rather than merely demonstrating that computational goals can be embedded in a Bayesian model that renders a particular behavior or process rational. Presented here is a fusion system based on simple, low-cost computer vision and radiological sensors for tracking of multiple objects and identifying potential radiological materials being transported or shipped.
The main focus of this work is the development of calibration algorithms for characterizing the fused sensor system as a single entity.