MODFAT: Moving Object Detection by Removing Shadow Based on Fuzzy Technique with an Adaptive Thresholding Method

Nowadays, in this technology centric world, gadgets have become handy due to miniaturization. Especially cameras are widely used device for many aspects, one of the common applications is human behavior identification and intelligent video surveillance. In a such application moving object detection in complex dynamic scene is a tedious task due to various challenges such as occlusion, background illumination variation and shadow. Shadows are created in light occlusion in the object it has major impact in accurate object detection. In this paper, object detection with elimination of shadow is addressed. Many existing methods have failed in discriminating the actual moving object from shadow object very accurately. In order to overcome the limitations of existing methods, an improved fuzzy technique rule is used for shadow removal and an adaptive fuzzy thresholding is used for segmenting a foreground object in background. The proposed techniques are experimented with standard and our own datasets and also, it is compared with other existing approaches. Results of proposed method shows improved reliability.

Spot Tracking and TDC Sharing in SPAD Arrays for TOF LiDAR

Light Detection and Ranging (LiDAR) is a widespread technique for 3D ranging and has widespread use in most automated systems that must interact with the external environment, for instance in industrial and security applications. In this work, we study a novel architecture for Single Photon Avalanche Diode (SPAD) arrays suitable for handheld single point rangefinders, which is aimed at the identification of the objects’ position in the presence of strong ambient background illumination. The system will be developed for an industrial environment, and the array targets a distance range of about 1 m and a precision of few centimeters. Since the laser spot illuminates only a small portion of the array, while all pixels are exposed to background illumination, we propose and validate through Monte Carlo simulations a novel architecture for the identification of the pixels illuminated by the laser spot to perform an adaptive laser spot tracking and a smart sharing of the timing electronics, thus significantly improving the accuracy of the distance measurement. Such a novel architecture represents a robust and effective approach to develop SPAD arrays for industrial applications with extremely high background illumination.

Assessment of Background Illumination Influence on Accuracy of Measurements Performed on Optical Coordinate Measuring Machine Equipped with Video Probe

Currently the Coordinate Measuring Technique is facing new challenges both in terms of used methodology and a speed of measurement. More and more often modern optical systems or multisensor systems replace classic solutions. Measurement performed using the optical system is more vulnerable to incorrect points acquisition due to such factors as an inadequate focus or parameters of applied illumination. This article examines the effect of an increasing illumination on the measurement result. A glass reference plate with marked circles and a hole plate standard were used for the measurements performed on a multi-sensor machine Zeiss O’ Inspect 442. The experiment consisted of measurements of standard objects with different values of the backlight at the maximum magnification. Such approach allows to assess the influence of controlled parameter on errors of diameter and form measurements as well as an uncertainty of measurements by determination of ellipses of point repeatability. The analysis of the obtained results shows that increasing backlight mainly affects the result of the diameter measurement.

INSTALLATION FOR PHYSICAL SIMULATION OF LASER LOCATION PROCESSES

A device for physical modeling of laser ranging processes has been developed, taking into account aerosol interference phenomena of natural and artificial origin and active background illumination. The installation simulates the processes of object detection and recognition by a laser information-measuring system (LIMS) under conditions of external destabilizing factors and obstacles in the atmospheric channel.

Development of smartphone-based imaging techniques for the estimation of chlorophyll content in lettuce leaves

Leaf color is a good indicator of plant’s health status. In this study, a new image acquisition technique was developed to estimate chlorophyll content of lettuce leaves. The images of lettuce leaves grown under artificial light were acquired using a smartphone. Leaves images was captured by directly attached the leaves to the camera lens with the aid of background illumination from SMD LED. Red, green, blue (RGB) color indices were extracted from leaves color images and some vegetation indices were also calculated. Then, the correlation between these indices and chlorophyll content obtained from SPAD502 chlorophyll meter were evaluated. Significant correlation was found between all the image indices and chlorophyll content with the R2 ranging from 0.63 to 0.85 except for G and B indices from RGB component. Highly significant correlation was found between vegetation indices (VI) and chlorophyll content (R2 = 0.85) with the lowest root mean square error (RMSE) of 8.07 g of chlorophyll/100 g fresh tissue. This demonstrated that the chlorophyll content of lettuce leaves can be successfully estimated using regular smartphone with added background light illumination from SMD LED.

An Improved Deep Mutual-Attention Learning Model for Person Re-Identification

Person re-identification is the task of matching pedestrian images across a network of non-overlapping camera views. It poses aggregated challenges resulted from random human pose, clutter from the background, illumination variations, and other factors. There has been a vast number of studies in recent years with promising success. However, key challenges have not been adequately addressed and continue to result in sub-optimal performance. Attention-based person re-identification gains more popularity in identifying discriminatory features from person images. Its potential in terms of extracting features common to a pair of person images across the feature extraction pipeline has not been be fully exploited. In this paper, we propose a novel attention-based Siamese network driven by a mutual-attention module decomposed into spatial and channel components. The proposed mutual-attention module not only leads feature extraction to the discriminative part of individual images, but also fuses mutual features symmetrically across pairs of person images to get informative regions common to both input images. Our model simultaneously learns feature embedding for discriminative cues and the similarity measure. The proposed model is optimized with multi-task loss, namely classification and verification loss. It is further optimized by a learnable mutual-attention module to facilitate an efficient and adaptive learning. The proposed model is thoroughly evaluated on extensively used large-scale datasets, Market-1501 and Duke-MTMC-ReID. Our experimental results show competitive results with the state-of-the-art works and the effectiveness of the mutual-attention module.