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Visible and near-infrared(NIR) band sensors provide images that capture complementary spectral radiations from a scene. And the fusion of the visible and NIR image aims at utilizing their spectrum properties to enhance image quality.…

This study proposes a semi-supervised co-training framework for object detection in densely packed retail environments, where limited labeled data and complex conditions pose major challenges. The framework combines Faster R-CNN (utilizing…

Binarized statistical image features (BSIF) have been successfully used for texture analysis in many computer vision tasks, including iris recognition and biometric presentation attack detection. One important point is that all applications…

Despite the recent advances in automatically describing image contents, their applications have been mostly limited to image caption datasets containing natural images (e.g., Flickr 30k, MSCOCO). In this paper, we present a deep learning…

Iris recognition is widely used in high-security scenarios due to its stability and distinctiveness. However, iris images captured by different devices exhibit certain and device-related consistent differences, which has a greater impact on…

Detecting anomalies in hyperspectral image data, i.e. regions which are spectrally distinct from the image background, is a common task in hyperspectral imaging. Such regions may represent interesting objects to human operators, but…

Tag-based image retrieval (TBIR) has drawn much attention in recent years due to the explosive amount of digital images and crowdsourcing tags. However, TBIR is still suffering from the incomplete and inaccurate tags provided by users,…

Encouraged by the success of deep learning in a variety of domains, we investigate a novel application of its methods on the effectiveness of detecting user confusion in eye-tracking data. We introduce an architecture that uses RNN and CNN…

Automatic annotation of images with descriptive words is a challenging problem with vast applications in the areas of image search and retrieval. This problem can be viewed as a label-assignment problem by a classifier dealing with a very…

Iris recognition is widely used in several fields such as mobile phones, financial transactions, identification cards, airport security, international border control, voter registration for living persons. However, the possibility of…

This paper proposes the first, known to us, open source presentation attack detection (PAD) solution to distinguish between authentic iris images (possibly wearing clear contact lenses) and irises with textured contact lenses. This software…

We introduce a new dataset for graphical object detection in business documents, more specifically annual reports. This dataset, IIIT-AR-13k, is created by manually annotating the bounding boxes of graphical or page objects in publicly…

Gender classification has emerged as a crucial aspect in various fields, including security, human-machine interaction, surveillance, and advertising. Nonetheless, the accuracy of this classification can be influenced by factors such as…

Smartphone-based iris recognition in the visible spectrum (VIS) remains difficult due to illumination variability, pigmentation differences, and the absence of standardized capture controls. This work presents a compact end-to-end pipeline…

Annotating user interfaces (UIs) that involves localization and classification of meaningful UI elements on a screen is a critical step for many mobile applications such as screen readers and voice control of devices. Annotating object…

This article compares the performance of six prominent object detection algorithms, YOLOv11, RetinaNet, Fast R-CNN, YOLOv8, RT-DETR, and DETR, on the NEU-DET surface defect detection dataset, comprising images representing various metal…

Iris is one of the popular biometrics that is widely used for identity authentication. Different features have been used to perform iris recognition in the past. Most of them are based on hand-crafted features designed by biometrics…

We present a flexible and high-performance framework, named Pyramid R-CNN, for two-stage 3D object detection from point clouds. Current approaches generally rely on the points or voxels of interest for RoI feature extraction on the second…

Automated object detection has become increasingly valuable across diverse applications, yet efficient, high-quality annotation remains a persistent challenge. In this paper, we present the development and evaluation of a platform designed…

Object detection in natural scenes can be a challenging task. In many real-life situations, the visible spectrum is not suitable for traditional computer vision tasks. Moving outside the visible spectrum range, such as the thermal spectrum…