Contact-based biometric applications primarily use prints from a finger or a palm for a single instance in different applications. For access control, there is an enrollment process using one or more templates which are compared with verification images. In forensics applications, randomly located, partial, and often degraded prints acquired from a crime scene are compared with the images captured from suspects or existing fingerprint databases, like AFIS. In both scenarios, if we need to use handprints which include segments from the finger and palm, what would be the solution? The motivation behind this is the concept of one single algorithm for one hand. Using an algorithm that can incorporate both prints in a common processing framework can be an alternative which will have advantages like scaling to larger existing databases. This work proposes a method that uses minutiae or minutiae-like features, Delaunay triangulation and graph matching with invariant feature representation to overcome the effects of rotation and scaling. Since palm prints have a large surface area with degradation, they tend to have many false minutiae compared to fingerprints, and the existing palm print algorithms fail to tackle this. The proposed algorithm constructs Delaunay triangulated graphs (DTG) using minutiae where Delaunay triangles form from minutiae, and initiate a collection of base triangles for opening the matching process. Several matches may be observed for a single triangle match when two images are compared. Therefore, the set of initially matched triangles may not be a true set of matched triangles. Each matched triangle is then used to extend as a sub-graph, adding more nodes to it until a maximum graph size is reached. When a significant region of the template image is matched with the test image, the highest possible order of this graph will be obtained. To prove the robustness of the algorithm to geometrical variations and working ability with extremely degraded (similar to latent prints) conditions, it is demonstrated with a subset of partial-quality and extremely-low-quality images from the FVC (fingerprint) and the THUPALMLAB (palm print) databases with and without geometrical variations. The algorithm is useful when partial matches between template and test are expected, and alignment or geometrical normalization is not accurately possible in pre-processing. It will also work for cross-comparisons between images that are not known a priori. Full article

This study presents a computational ghost imaging (GI) scheme that utilizes sequenced random speckle pattern illumination. The primary objective is to develop a speckle pattern/sequence that improves computational time without compromising image quality. To achieve this, we modulate the sequence of speckle sizes and design experiments based on three sequence rules for ordering the random speckle patterns. Through theoretical analysis and experimental validation, we demonstrate that our proposed scheme achieves a significantly better contrast-to-noise rate (CNR) compared to traditional GI at a similar resolution. Notably, the sequential GI method outperforms conventional approaches by providing over 10 times faster computational speed in certain speckle composition groups. Furthermore, we identify the corresponding speckle sizes that yield superior image quality, which are found to be geometrically proportional to the reference object area. This innovative approach utilizing sequenced random speckle patterns demonstrates potential suitability for imaging objects with complex or unknown shapes. The findings of this study hold great promise for advancing the field of computational GI and pseudo-thermal GI, addressing the need for improved computational efficiency while maintaining high-quality imaging. Full article

The semantic segmentation of point clouds has significant applications in fields such as autonomous driving, robot vision, and smart cities. As LiDAR technology continues to develop, point clouds have gradually become the main type of 3D data. However, due to the disordered and scattered nature of point cloud data, it is challenging to effectively segment them semantically. A three-dimensional (3D) shape provides an important means of studying the spatial relationships between different objects and their structures in point clouds. Thus, this paper proposes a semi-supervised semantic segmentation network for point clouds based on 3D shape, which we call SBSNet. This network groups and encodes the geometric information of 3D objects to form shape features. It utilizes an attention mechanism and local information fusion to capture shape context information and calculate the data features. The experimental results showed that the proposed method achieved an overall intersection ratio of 85.3% in the ShapeNet dataset and 90.6% accuracy in the ModelNet40 dataset. Empirically, it showed strong performance on par or even better than state-of-the-art models. Full article

The rapid increase of space debris poses a risk to space activities, so it is vital to develop countermeasures in terms of space surveillance to prevent possible threats. The current Space Surveillance Network is majorly composed of radar and optical telescopes that regularly observe and track space objects. However, these measures are limited by size, being able to detect only a tiny amount of debris. Hence, alternative solutions are essential for securing the future of space activities. Therefore, this paper proposes the design of a payload camera breadboard for space surveillance to increase the information on debris, particularly for the under-catalogued ones. The device was designed with similar characteristics to star trackers of small satellites and CubeSats. Star trackers are attitude devices usually used in satellites for attitude determination and, therefore, have a wide potential role as a major tool for space debris detection. The breadboard was built with commercial off-the-shelf components, representing the current space-camera resolution and field of view. The image sensor was characterized to compute the sensitivity of the camera and evaluate the detectability performance in several simulated positions. Furthermore, the payload camera concept was tested by taking images of the night sky using satellites as proxies of space debris, and a photometric analysis was performed to validate the simulated detectability performance. Full article

Applying edge preservation filters for cost aggregation has been a leading technique in generating dense disparity maps. However, traditional approaches usually require intensive calculations, and their design parameters must be tuned for different scenarios to obtain the best performance. This paper shows that a simple texture-independent aggregation approach can achieve similar high performance. The proposed algorithm is equivalent to a sequence of matrix multiplications involving two weighting matrices and a primary matching cost. Notably, the weighting matrices are constant for image pairs with the same resolution. For higher matching accuracy, we integrate the algorithm with a multi-scale scheme to fully exploit the spatial distribution of textures in the image pairs. The resultant hybrid approach is efficient and accurate enough to surpass most existing approaches in stereo matching. The performance of the proposed approach is verified by extensive simulation results using the Middlebury (3rd Edition) benchmark stereo database. Full article

Silently asymptomatic at an early stage and often painless, requiring only active surveillance, Prostate Cancer (PCa) is traditionally diagnosed by a Digital Rectal Examination (DRE) and a Prostate Specific Antigen (PSA) blood test. A histological examination, searching for pattern irregularities on the prostate glandular tissue, is performed to quantify the aggressiveness of PCa. The assigned Gleason Score (GS), usually combined with Transrectal Ultrasound Guided Biopsy (TRUS), allows the stratification of patients according to their risk group. Intermediate-risk patients may have a favourable (GS = 3 + 4) or unfavourable (GS = 4 + 3) prognosis. This borderline is critical for defining treatments and possible outcomes, while External Beam Radiotherapy (EBRT) is a curative option for localised and locally advanced disease and as a palliative option for metastatic low-volume disease; active surveillance or watchful waiting can also be an option for patients with a favourable prognosis. With radiomics, quantifying phenotypic characteristics in medical imaging is now possible. In the EBRT workflow, there are several imaging modalities, such as Magnetic Ressonance Imaging (MRI), Computed Tomography (CT), Positron Emission Tomography (PET), Ultrasound and Cone Beam Computed Tomography (CBCT). Most radiomic PCa studies focused on MRI and addressed tumour staging, GS, PSA or Biochemical Recurrence (BCR). This study intends to use CBCT radiomics to distinguish between favourable and unfavourable cases, with the potential of evaluating an ongoing treatment. Seven of the most used feature selection methods, combined with 14 different classifiers, were evaluated in a total of 98 pipelines. From those, six stood out with Area Under the Receiver Operating Characteristic (AUROC) values ≥ 0.79. To the best of our knowledge, this is the first work to evaluate a PCa favourable vs. unfavourable prognosis model based on CBCT radiomics. Full article