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Visual inspection of printed circuit boards has generally depended on human inspectors. However, a system has been developed which allows for automated visual inspection using robotics and modern image processing techniques. This paper first introduces automatic visual inspection processes, overviews the Automatic Board Assembly, Inspection and Test (ABAIT) system, reviews image processing concepts and describes the Circular Pipeline Video Processor (CPVP). Image data from the CPVP is analyzed and an investigation into alternate segmentation algorithms to identify circuit board features is presented. The relative performance of these algorithms is compared conclusions drawn.

This paper presents a generic pre-processor for expediting conventional template matching techniques. Instead of locating the best matched patch in the reference image to a query template via exhaustive search, the proposed algorithm rules out regions with no possible matches with minimum computational efforts. While working on simple patch features, such as mean, variance and gradient, the fast pre-screening is highly discriminative. Its computational efficiency is gained by using a novel octagonal-star-shaped template and the inclusion-exclusion principle to extract and compare patch features. Moreover, it can handle arbitrary rotation and scaling of reference images effectively, and also be robust to uniform illumination changes. GPU-aided implementation shows great efficiency of parallel computing in the algorithm design, and extensive experiments demonstrate that the proposed algorithm greatly reduces the search space while never missing the best match. / Thesis / Master of Applied Science (MASc)

Commonly experienced bad weather conditions like fog, snow and rain generate pixel intensity changes in images and videos taken in outdoor environment and impair the performance of algorithms in outdoor vision systems. Hence, the impact of bad weather conditions need to be processed to improve the performance of outdoor vision systems. This thesis focuses on three most common weather conditions: fog, snow and rain. Their physical properties are first analyzed. Based on their properties, traditional methods are introduced individually to remove these weather conditions' effect on images or videos. For fog removal, the scattering model is used to describe the fog scene in images and estimate the clear scene radiance from single input images. In this thesis two scenario are discussed, one with videos and the other with single images. The removal of snow and rain in videos is easier than in single images. In videos, temporal and chromatic properties of snow and rain can be used to remove their impact. While in single images, traditional methods with edge preserving filters were discussed. However, there are multiple limitations of traditional methods that are based on physical properties of bad weather conditions. Each of them can only deal with one specific weather condition at a time. In real application scenarios, it is difficult for vision systems to recognize different weather conditions and choose corresponding methods to remove them. Therefore, machine learning methods have advantages compared with traditional methods. In this thesis, Generative Adversarial Network (GAN) is used to remove the effect of these weather conditions. GAN performs the image to image translation instead of analyzing the physical properties of different weather conditions. It gets impressive results to deal with different weather conditions. / Thesis / Master of Applied Science (MASc)

No / A robust watermarking technique is implemented for copyright protection. The proposed method is based on 2-level discrete wavelet transform (DWT). The embedded watermarking information is a mobile phone number including the international code. The first level of the DWT transformation is applied on 16×16 blocks of the host image. All the coefficients of the 8×8 low-low (LL1) first level sub-band are grouped into one matrix. The second level of the DWT is then applied to the grouped matrix from the first level transformation. The highest coefficient from the LL2 sub-band (4×4) is used for embedding the watermark information. The extracting process is blind since it does not require the original image at the receiver side. The distortion in the host image due to the watermarking process is minimal and the PSNR is greater than 60 dB. The proposed algorithm showed robustness against several attacks such as scaling, filtering, cropping, additive noise and JPEG compression.

Digital image enhancement refers to the improvement of a given image for human interpretation. Digital image processing facilities are those in which hardware and software computing elements are combined in such a way as to enable the processing of digital images. This report describes the use of the Naval Training Equipment Center (NTEC) Computer Systems Laboratory computing facilities to enhance digital images. Described are two major hardware systems, the IKONAS RDS-3000 raster display graphics system and the VAX-11/780, and the digital image processing program (DIMPRP) written by the author. Digital image enhancement theory and practice are addressed through a discussion of the DIMPRP software. Finally, enhancements to the NTEC digital image processing facility such as improvements in hardware reliability, documentation, and increased speed of program execution are discussed.

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But haze removal is highly challenging due to its mathematical ambiguity, typically when the input is merely a single image. In this thesis, we propose a simple but effective image prior, called dark channel prior, to remove haze from a single image. The dark channel prior is a statistical property of outdoor haze-free images: most patches in these images should contain pixels which are dark in at least one color channel. Using this prior with a haze imaging model, we can easily recover high quality haze-free images. Experiments demonstrate that this simple prior is powerful in various situations and outperforms many previous approaches. / Haze is a natural phenomenon that obscures scenes, reduces visibility, and changes colors. It is an annoying problem for photographers since it degrades image quality. It is also a threat to the reliability of many applications, like outdoor surveillance, object detection, and aerial imaging. So removing haze from images is important in computer vision/graphics. / Speed is an important issue in practice. Like many computer vision problems, the time-consuming step in haze removal is to combine pixel-wise constraints with spatial continuities. In this thesis, we propose two novel techniques to solve this problem efficiently. The first one is an unconventional large-kernel-based linear solver. The second one is a generic edge-aware filter which enables real-time performance. This filter is superior in various applications including haze removal, in terms of speed and quality. / The human visual system is able to perceive haze, but the underlying mechanism remains unknown. In this thesis, we present new illusions showing that the human visual system is possibly adopting a mechanism similar to the dark channel prior. Our discovery casts new insights into human vision research in psychology and physiology. It also reinforces the validity of the dark channel prior as a computer vision algorithm, because a good way for artificial intelligence is to mimic human brains. / He, Kaiming. / Adviser: Xiaoou Tang. / Source: Dissertation Abstracts International, Volume: 73-06, Section: B, page: . / Thesis (Ph.D.)--Chinese University of Hong Kong, 2011. / Includes bibliographical references (leaves 131-138). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [201-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstract also in Chinese.