Review: computer vision applied to the inspection and quality control of fruits and vegetables

This is a review of the current existing literature concerning the inspection of fruits and vegetables with the application of computer vision, where the techniques most used to estimate various properties related to quality are analyzed. The objectives of the typical applications of such systems include the classification, quality estimation according to the internal and external characteristics, supervision of fruit processes during storage or the evaluation of experimental treatments. In general, computer vision systems do not only replace manual inspection, but can also improve their skills. In conclusion, computer vision systems are powerful tools for the automatic inspection of fruits and vegetables. In addition, the development of such systems adapted to the food industry is fundamental to achieve competitive advantages

Color detection in dermoscopic images of pigmented skin lesions through computer vision techniques

This thesis offers an insight into skin cancer detection, focusing on the extraction of distinct features (color, namely) from potential melanoma lesions. The following document provides an outlook of melanoma analysis, as well as experimental results based on Matlab implementations. The relevance of the work carried out throughout this project resides in the specificity of the study: color is a key characteristic in melanoma inspection. It is usually linked to pattern analysis but seldom the sole object of research. Most lines of work in the field of skin cancer diagnosis associate color with other features such as texture, shape, asymmetry or pattern of the lesion. Studies cement this belief regarding the vital significance of color, as the number of colors in a lesion happens to be the most significant biomarker for determining malignancy. Different image processing techniques will be applied to build statistical models that shape the outcome of the prospective diagnosis. The purpose of the project is the development of an assisting tool able to detect the most prevalent colors in skin pigmented lesions, in order to give a probabilistic result. The strength of this idea lies in the resemblance to actual medical procedures; dermatologists examine color to diagnose melanoma. Simulating medical proceedings is a burgeoning trend in CAD systems because it renders the advancements in this field more likely to be accepted by the medical community. An additional motivation comes from real-life statistics: skin cancer is, by far, the most frequent type of cancer. Moreover, although melanoma is the least common form of skin cancer at only around 1% of all cases, the majority of deaths related to skin cancer are due to melanoma. Furthermore, the rate of melanoma occurrence is particularly high in Spain and has significantly increased in the last decade, hence the importance of reliable diagnosis that is not exclusively contingent on the specialist’s subjective judgment.Ingeniería de Sistemas Audiovisuale

Real-time Model-based Image Color Correction for Underwater Robots

Recently, a new underwater imaging formation model presented that the coefficients related to the direct and backscatter transmission signals are dependent on the type of water, camera specifications, water depth, and imaging range. This paper proposes an underwater color correction method that integrates this new model on an underwater robot, using information from a pressure depth sensor for water depth and a visual odometry system for estimating scene distance. Experiments were performed with and without a color chart over coral reefs and a shipwreck in the Caribbean. We demonstrate the performance of our proposed method by comparing it with other statistic-, physic-, and learning-based color correction methods. Applications for our proposed method include improved 3D reconstruction and more robust underwater robot navigation.Comment: Accepted at the 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS

Hidden Two-Stream Convolutional Networks for Action Recognition

Analyzing videos of human actions involves understanding the temporal relationships among video frames. State-of-the-art action recognition approaches rely on traditional optical flow estimation methods to pre-compute motion information for CNNs. Such a two-stage approach is computationally expensive, storage demanding, and not end-to-end trainable. In this paper, we present a novel CNN architecture that implicitly captures motion information between adjacent frames. We name our approach hidden two-stream CNNs because it only takes raw video frames as input and directly predicts action classes without explicitly computing optical flow. Our end-to-end approach is 10x faster than its two-stage baseline. Experimental results on four challenging action recognition datasets: UCF101, HMDB51, THUMOS14 and ActivityNet v1.2 show that our approach significantly outperforms the previous best real-time approaches.Comment: Accepted at ACCV 2018, camera ready. Code available at https://github.com/bryanyzhu/Hidden-Two-Strea

Low levels of specularity support operational color constancy, particularly when surface and illumination geometry can be inferred

We tested whether surface specularity alone supports operational color constancy—the ability to discriminate changes in illumination or reflectance. Observers viewed short animations of illuminant or reflectance changes in rendered scenes containing a single spherical surface and were asked to classify the change. Performance improved with increasing specularity, as predicted from regularities in chromatic statistics. Peak performance was impaired by spatial rearrangements of image pixels that disrupted the perception of illuminated surfaces but was maintained with increased surface complexity. The characteristic chromatic transformations that are available with nonzero specularity are useful for operational color constancy, particularly if accompanied by appropriate perceptual organization