Probabilistic Intersection-Over-Union for Training and Evaluation of Oriented Object Detectors.

Oriented object detection is a challenging and relatively new problem. Most existing approaches are based on deep learning and explore Oriented Bounding Boxes (OBBs) to represent the objects. They are typically based on adaptations of traditional detectors that work with Horizontal Bounding Boxes (HBBs), which have been exploring IoU-like loss functions to regress the HBBs.

Omnidirectional 2.5D representation for COVID-19 diagnosis using chest CTs.

The Coronavirus Disease 2019 (COVID-19) has drastically overwhelmed most countries in the last two years, and image-based approaches using computerized tomography (CT) have been used to identify pulmonary infections. Recent methods based on deep learning either require time-consuming per-slice annotations (2D) or are highly data- and hardware-demanding (3D). This work proposes a novel omnidirectional 2.

Deep Multi-Scale Feature Learning for Defocus Blur Estimation.

This paper presents an edge-based defocus blur estimation method from a single defocused image. We first distinguish edges that lie at depth discontinuities (called depth edges, for which the blur estimate is ambiguous) from edges that lie at approximately constant depth regions (called pattern edges, for which the blur estimate is well-defined). Then, we estimate the defocus blur amount at pattern edges only, and explore an interpolation scheme based on guided filters that prevents data propagation across the detected depth edges to obtain a dense blur map with well-defined object boundaries.

A Hierarchical Superpixel-Based Approach for DIBR View Synthesis.

View synthesis allows observers to explore static scenes using aligned color images and depth maps captured in a preset camera path. Among the options, depth-image-based rendering (DIBR) approaches have been effective and efficient since only one pair of color and depth map is required, saving storage and bandwidth. The present work proposes a novel DIBR pipeline for view synthesis that properly tackles the different artifacts that arise from 3D warping, such as cracks, disocclusions, ghosts, and out-of-field areas.

The Subcortical-Allocortical- Neocortical for the Emergence and Morphological Heterogeneity of Pyramidal Neurons in the Human Brain.

Human cortical and subcortical areas integrate emotion, memory, and cognition when interpreting various environmental stimuli for the elaboration of complex, evolved social behaviors. Pyramidal neurons occur in developed phylogenetic areas advancing along with the allocortex to represent 70-85% of the neocortical gray matter. Here, we illustrate and discuss morphological features of heterogeneous spiny pyramidal neurons emerging from specific amygdaloid nuclei, in CA3 and CA1 hippocampal regions, and in neocortical layers II/III and V of the anterolateral temporal lobe in humans.

Edge-Based Defocus Blur Estimation With Adaptive Scale Selection.

Objects that do not lie at the focal distance of a digital camera generate defocused regions in the captured image. This paper presents a new edge-based method for spatially varying defocus blur estimation using a single image based on reblurred gradient magnitudes. The proposed approach initially computes a scale-consistent edge map of the input image and selects a local reblurring scale aiming to cope with noise, edge mis-localization, and interfering edges.

Structure and diversity of human dendritic spines evidenced by a new three-dimensional reconstruction procedure for Golgi staining and light microscopy.

Background: Different approaches aim to unravel detailed morphological features of neural cells. Dendritic spines are multifunctional units that reflect cellular connectivity, synaptic strength and plasticity.

New Method: A novel three-dimensional (3D) reconstruction procedure is introduced for visualization of dendritic spines from human postmortem brain tissue using brightfield microscopy.

Stereo matching and view interpolation based on image domain triangulation.

This paper presents a new approach for stereo matching and view interpolation problems based on triangular tessellations suitable for a linear array of rectified cameras. The domain of the reference image is initially partitioned into triangular regions using edge and scale information, aiming to place vertices along image edges and increase the number of triangles in textured regions. A region-based matching algorithm is then used to find an initial disparity for each triangle, and a refinement stage is applied to change the disparity at the vertices of the triangles, generating a piecewise linear disparity map.

A new methodology for detection of counterfeit Viagra® and Cialis® tablets by image processing and statistical analysis.

This paper proposes a new approach for automatic classification of counterfeit Viagra(®) and Cialis(®) tablets using image processing and statistical analysis. A high resolution VSC 5000 is used for image acquisition in a controlled environment, and the combination of a thresholding technique with morphological operators is used to segment the tablet from the background. A statistical model based on the RGB color components of original samples is built, and the detection of counterfeit tablets was performed by checking the adherence of a test sample to the obtained distribution using the Bhattacharyya distance.

Adaptive image denoising using scale and space consistency.

This paper proposes a new method for image denoising with edge preservation, based on image multiresolution decomposition by a redundant wavelet transform. In our approach, edges are implicitly located and preserved in the wavelet domain, whilst image noise is filtered out. At each resolution level, the image edges are estimated by gradient magnitudes (obtained from the wavelet coefficients), which are modeled probabilistically, and a shrinkage function is assembled based on the model obtained.

Denoising and enhancing digital mammographic images for visual screening.

Dense regions in digital mammographic images are usually noisy and have low contrast, and their visual screening is difficult. This paper describes a new method for mammographic image noise suppression and enhancement, which can be effective particularly for screening image dense regions. Initially, the image is preprocessed to improve its local contrast and the discrimination of subtle details.