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Deep neural networks (DNNs) achieve excellent performance on standard classification tasks. However, under image quality distortions such as blur and noise, classification accuracy becomes poor. In this work, we compare the performance of DNNs with human subjects on distorted images. We show that, although DNNs perform better than or on par with humans on good quality images, DNN performance is still...
Automated inspection systems have been used extensively for high-speed defect detection, gaging and quality control. In the semiconductor manufacturing industry, assembly and testing processes are getting more complex resulting in a greater tendency of defects to impact the production process. Currently available defect detection and classification systems are customized and hard-wired to the detection...
Image quality is an important practical challenge that is often overlooked in the design of machine vision systems. Commonly, machine vision systems are trained and tested on high quality image datasets, yet in practical applications the input images can not be assumed to be of high quality. Recently, deep neural networks have obtained state-of-the-art performance on many machine vision tasks. In...
Stereoscopic high definition video is one of the promising next-generation video services because 3D video can express more life-like visual experiences. 3D video quality of experience is affected by the employed codec and video format. Therefore, we conducted extensive subjective assessments for side-by-side and frame-sequential video sequences using different codecs. In this paper, we first show...
Side information (SI) generation plays a key-role in determining the performance of the Distributed Video Coding System (DVC). Current approaches to DVC rely on motion-compensated interpolation (MCTI) to generate at the decoder the SI which is an estimation of the frame being decoded. This work presents a novel MCTI algorithm. In the proposed scheme, motion estimation utilizes only the low-frequency...
Visual imaging methods have been lately extensively used in applications that are targeted to understand and analyze botanical patterns. There is a rich literature on imaging applications in the above field and various techniques have been developed. In this paper, we introduce a fully automated imaging approach for extracting spatial vein pattern data from leaf images, such as vein densities but...
Due to the fast growing market for embedded DSP applications, there is a need for electrical engineers with expertise in the field. This paper is concerned with how to expand the real-time DSP course currently offered to on-campus students at Arizona State University so that it can be offered to online students working in industry or enrolled at other academic institutions. In contrast to many courses,...
Arizona State University's electrical engineering department is redesigning its Freshman introduction to engineering design course with the objective of increasing students' retention and the students' interest in engineering careers. National instruments (NI) products, including the LabVIEWtrade embedded DSP module, which supports embedded real-time solutions, the SPEEDY-33 DSP board, and the NI...
This work is motivated by the fact that existing no-reference sharpness metrics fail in predicting the correct amount of blurriness in images with different contexts. This paper presents a no-reference objective sharpness metric that can be applied to images with different contexts. The metric combines a human visual system (HVS)-based sharpness perception model as well as a local features extractor...
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