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Technologies currently used for cotton contaminant assessment suffer from some fundamental limitations. These limitations result in the misassessment of the cotton quality, and have a serious impact on its economic value. Through our research, we have shown that X-ray microtomographic image analysis may be applied with a high degree of success to noninvasive evaluation of cotton for the recognition of contaminants. We believe that this procedure, when realized in real time, will have a serious impact on the cotton cleaning process, and indeed on the economic value of cotton. Index Terms--Cotton trash, fuzzy classifier, image analysis, pattern classification, volumetric data, X-ray tomography.

Identification of cell death mechanisms, particularly distinguishing between apoptotic versus nonapoptotic pathways, is of paramount importance for a wide range of applications related to cell signaling, interaction with pathogens, therapeutic processes, drug discovery, drug resistance, and even pathogenesis of diseases like cancers and neurogenerative disease among others. Here, we present a novel high‐throughput method of identifying apoptotic versus necrotic versus other nonapoptotic cell death processes, based on lensless digital holography. This method relies on identification of the temporal changes in the morphological features of mammalian cells, which are unique to each cell death processes. Different cell death processes were induced by known cytotoxic agents. A deep learning‐based approach was used to automatically classify the cell death mechanism (apoptotic vs necrotic vs nonapoptotic) with more than 93% accuracy. This label free approach can provide a low cost (<$250) alternative to some of the currently available high content imaging‐based screening tools. [ABSTRACT FROM AUTHOR]

A unifying approach to the derivation and implementation of a shift-invariant wavelet transform of one- and two-dimensional discrete signals is presented. An analytical process generates a shift-invariant orthogonal, discrete wavelet transform called the multiscale wavelet representation (MSWAR). The derived MSWAR is invertible which makes it particularly useful in image registration and fusion and object recognition. Its implementation is equivalent to filter upsampling technique while its computational complexity is quantified in terms of required convolution.

This paper describes a vision-based fabric inspection system that accomplishes on-loom inspection of the fabric under construction with 100% coverage. The inspection system, which offers a scalable open architecture, can be manufactured at relatively low cost using off-the-shelf components. While synchronized to the motion of the loom, the developed system first acquires very high-quality vibration-free images of the fabric using either front or backlighting. Then, the acquired images are subjected to a novel defect segmentation algorithm, which is based on the concepts of wavelet transform, image fusion, and the correlation dimension. The essence of this segmentation algorithm is the localization of those events (i.e., defects) in the input images that disrupt the global homogeneity of the background texture. The efficacy of this algorithm, as well as the overall inspection system, has been tested thoroughly under realistic conditions. The system was used to acquire and to analyze more than 3700 images of fabrics that were constructed with two different types of yarn. In each case, the performance of the system was evaluated as an operator introduced defects from 26 categories into the weaving process. The overall detection rate of the presented approach was found to be 89% with a localization accuracy of 0.2 in (i.e., the minimum defect size) and a false alarm rate of 2.5%. Index Terms--Computer vision, defect detection, fabric inspection, wavelet transform.