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A binocular stereo vision measurement system is widely used in fields such as industrial inspection and marine engineering due to its high accuracy, low cost, and ease of deployment. An unreasonable structural design can lead to difficulties in image matching and inaccuracies in depth computation during subsequent processing, thereby limiting the system's performance and applicability. This paper establishes a systemic error analysis model to enable the validation of changes in structural parameters on the performance of the binocular vision measurement. Specifically, the impact of structural parameters such as baseline distance and object distance on measurement error is analyzed. Extensive experiments reveal that when the ratio of baseline length to object distance is between 1 and 1.5, and the angle between the baseline and the optical axis is between 30 and 40 degrees, the system measurement error is minimized. The experimental conclusions provide guidance for subsequent measurement system research and parameter design. [ABSTRACT FROM AUTHOR]

Lip‐reading is a process of interpreting speech by visually analysing lip movements. Recent research in this area has shifted from simple word recognition to lip‐reading sentences in the wild. This paper attempts to use phonemes as a classification schema for lip‐reading sentences to explore an alternative schema and to enhance system performance. Different classification schemas have been investigated, including character‐based and visemes‐based schemas. The visual front‐end model of the system consists of a Spatial‐Temporal (3D) convolution followed by a 2D ResNet. Transformers utilise multi‐headed attention for phoneme recognition models. For the language model, a Recurrent Neural Network is used. The performance of the proposed system has been testified with the BBC Lip Reading Sentences 2 (LRS2) benchmark dataset. Compared with the state‐of‐the‐art approaches in lip‐reading sentences, the proposed system has demonstrated an improved performance by a 10% lower word error rate on average under varying illumination ratios. [ABSTRACT FROM AUTHOR]

This article focuses on the extraction of information from human skin and scalp hair for evaluation of a subject's condition in the cosmetic and pharmaceutical industries. It uses capacitive images from existing hand-held research equipment and it applies image processing algorithms to expand their possible applications. The literature review introduces the readers into the field of skin research, and it highlights pieces of information that can be extracted by in vivo skin and ex vivo hair measurements. Then, the selected scientific equipment is presented, and Maxwell-based electrostatic simulations are employed to evaluate the measurement apparatus. Image analysis algorithms are suggested for (a) the detection of polygons on the human skin texture, (b) the estimation of wrinkles length and (c) the observation of hair water sorption capabilities by capacitive imaging systems. Finally, experiments are conducted to evaluate the performance of the presented algorithms and the results are compared with the literature. The results indicate that capacitive imaging systems can be used for skin age classification, detection and tracking of skin artifacts (e.g., wrinkles, moles or scars) and calculation of water content in hair samples. [ABSTRACT FROM AUTHOR]

Background: In this study, the performance of capacitive imagining in skin micro‐relief analysis was investigated. This measurement principle has been used for skin hydration measurements over the last decade and it is commercially available by various manufacturers. Strengthening its potential for new applications could offer an affordable and portable multi‐purpose device for in vivo skin research. Previous studies in the literature have used a wide range of optical devices to determine how the skin surface topographic features are affected by chronological age, environmental influences and living habits. Material and methods: A capacitive system was used in order to capture hydration images from the middle volar forearm of twelve volunteers. The visual output of the system was studied and image processing algorithms were adapted to automatically extract skin micro‐relief features. The change in the skin network of lines during arm extension, the lines' anisotropy index and the number of closed polygons per skin surface area were plotted against the subjects' chronological age. The results were compared with optical measurements from the literature to validate our algorithms and evaluate the capacitive imaging in skin micro‐relief analysis. Results: The change in the intensity of primary and secondary lines during arm extension and the number of closed polygons per surface area were in agreement with the literature. The anisotropy index output gave inconclusive results. Conclusions: The experimental results show that the capacitive systems could only extract two‐dimensional skin topographic features. [ABSTRACT FROM AUTHOR]