The role of contextual materials in object recognition

While scene context is known to facilitate object recognition, little is known about which contextual “ingredients” are at the heart of this phenomenon. Here, we address the question of whether the materials that frequently occur in scenes (e.g., tiles in a bathroom) associated with specific objects (e.g., a perfume) are relevant for the processing of that object. To this end, we presented photographs of consistent and inconsistent objects (e.g., perfume vs. pinecone) superimposed on scenes (e.g., a bathroom) and close-ups of materials (e.g., tiles). In Experiment 1, consistent objects on scenes were named more accurately than inconsistent ones, while there was only a marginal consistency effect for objects on materials. Also, we did not find any consistency effect for scrambled materials that served as color control condition. In Experiment 2, we recorded event-related potentials and found N300/N400 responses—markers of semantic violations—for objects on inconsistent relative to consistent scenes. Critically, objects on materials triggered N300/N400 responses of similar magnitudes. Our findings show that contextual materials indeed affect object processing—even in the absence of spatial scene structure and object content—suggesting that material is one of the contextual “ingredients” driving scene context effects.

3D Mesh Pre-processing Method Based on Feature Point Classification and Anisotropic Vertex Denoising Considering Scene Structure Characteristics

3D mesh denoising plays an important role in 3D model pre-processing and repair. A fundamental challenge in the mesh denoising process is to accurately extract features from the noise and to preserve and restore the scene structure features of the model. In this paper, we propose a novel feature-preserving mesh denoising method, which was based on robust guidance normal estimation, accurate feature point extraction and an anisotropic vertex denoising strategy. The methodology of the proposed approach is as follows: (1) The dual weight function that takes into account the angle characteristics is used to estimate the guidance normals of the surface, which improved the reliability of the joint bilateral filtering algorithm and avoids losing the corner structures; (2) The filtered facet normal is used to classify the feature points based on the normal voting tensor (NVT) method, which raised the accuracy and integrity of feature classification for the noisy model; (3) The anisotropic vertex update strategy is used in triangular mesh denoising: updating the non-feature points with isotropic neighborhood normals, which effectively suppressed the sharp edges from being smoothed; updating the feature points based on local geometric constraints, which preserved and restored the features while avoided sharp pseudo features. The detailed quantitative and qualitative analyses conducted on synthetic and real data show that our method can remove the noise of various mesh models and retain or restore the edge and corner features of the model without generating pseudo features.

SASMITA GENDHING: Dulu dan Kini.

ABSTRACTSasmita gendhing is a series of words that lead to the name gendhing-gendhing as a supporter of the wayang kulit purwa show. While the wayang kulit show format still refers to the format of last night's show with the genre of palace performance, the gendhing grade became a bridge between the mastermind and the singer to sound the gendhing as a support for certain scenes. In the development of the existence of gendhing-gendhing grades are slowly declining, even the majority of masterminds no longer use gendhing grades. The text-context approach from HeddyShri Ahimsa is used to reveal the variety of gendhing grades and to find the cause of the decline in the use of gendhing grades. The study conducted found various forms of gendhing grades, among them, are figurative words, wangsalan, and cangkriman. While the cause of the decline in the use of gendhing grades is noted due to the change of image, freedom of scene structure, freedom of choice of play, and cross-style performance. Keywords: gendhing grades, text-context, performance ABSTRAKSasmita gendhing merupakan rangkaian kata yang mengarah pada nama gendhing-gendhing sebagai pendukung pertunjukan wayang kulit purwa. Ketika format pertunjukan wayang kulit masih mengacu pada format pertunjukan semalam dengan genre pakeliran istana, sasmita gendhing menjadi jembatan antara dalang dengan pengrawit untuk membunyikan gendhing sebagai pendukung adegan tertentu. Dalam perkembangannya eksistensi sasmita gendhing- gendhing perlahan surut, bahkan mayoritas dalang tidak lagi menggunakan sasmita gendhing. Pendekatan teks-konteks dari HeddyShri Ahimsa digunakan untuk mengungkap ragam sasmita gendhing serta mencari penyebab surutnya penggunaan sasmita gendhing. Dari kajian yang dilakukan ditemukan beragam bentuk sasmita gendhing, di antaranya adalah kata kiasan, wangsalan, dan cangkriman. Sedangkan penyebab surutnya penggunaan sasmita gendhing ditengarai karena adanya perubahan citra, kebebasan struktur adegan, kebebasan pemilihan lakon, dan silang gaya pakeliran. Kata kunci: sasmita gendhing, teks-konteks, pakeliran

Deep Learning Point Cloud Odometry

Achieving persistent and reliable autonomy for mobile robots in challenging field mission scenarios is a long-time quest for the Robotics research community. Deep learning-based LIDAR odometry is attracting increasing research interest as a technological solution for the robot navigation problem and showing great potential for the task.In this work, an examination of the benefits of leveraging learning-based encoding representations of real-world data is provided. In addition, a broad perspective of emergent Deep Learning robust techniques to track motion and estimate scene structure for real-world applications is the focus of a deeper analysis and comprehensive comparison.Furthermore, existing Deep Learning approaches and techniques for point cloud odometry tasks are explored, and the main technological solutions are compared and discussed.Open challenges are also laid out for the reader, hopefully offering guidance to future researchers in their quest to apply deep learning to complex 3D non-matrix data to tackle localization and robot navigation problems.

Coherent natural scene structure facilitates the extraction of task-relevant object information in visual cortex

Looking for objects within complex natural environments is a task everybody performs multiple times each day. In this study, we explore how the brain uses the typical composition of real-world environments to efficiently solve this task. We recorded fMRI activity while participants performed two different categorization tasks on natural scenes. In the object task, they indicated whether the scene contained a person or a car, while in the scene task, they indicated whether the scene depicted an urban or a rural environment. Critically, each scene was presented in an “intact” way, preserving its coherent structure, or in a “jumbled” way, with information swapped across quadrants. In both tasks, participants’ categorization was more accurate and faster for intact scenes. These behavioral benefits were accompanied by stronger responses to intact than to jumbled scenes across high-level visual cortex. To track the amount of object information in visual cortex, we correlated multivoxel response patterns during the two categorization tasks with response patterns evoked by people and cars in isolation. We found that object information in object- and body-selective cortex was enhanced when the object was embedded in an intact, rather than a jumbled scene. However, this enhancement was only found in the object task: When participants instead categorized the scenes, object information did not differ between intact and jumbled scenes. Together, these results indicate that coherent scene structure facilitates the extraction of object information in a task-dependent way, suggesting that interactions between the object and scene processing pathways adaptively support behavioral goals.

Image-Level Structure Recognition Using Image Features, Templates, and Ensemble of Classifiers

Image-level structural recognition is an important problem for many applications of computer vision such as autonomous vehicle control, scene understanding, and 3D TV. A novel method, using image features extracted by exploiting predefined templates, each associated with individual classifier, is proposed. The template that reflects the symmetric structure consisting of a number of components represents a stage—a rough structure of an image geometry. The following image features are used: a histogram of oriented gradient (HOG) features showing the overall object shape, colors representing scene information, the parameters of the Weibull distribution features, reflecting relations between image statistics and scene structure, and local binary pattern (LBP) and entropy (E) values representing texture and scene depth information. Each of the individual classifiers learns a discriminative model and their outcomes are fused together using sum rule for recognizing the global structure of an image. The proposed method achieves an 86.25% recognition accuracy on the stage dataset and a 92.58% recognition rate on the 15-scene dataset, both of which are significantly higher than the other state-of-the-art methods.

Underwater Image Enhancement using Color Balance and Fusion

We have introduced an elective way to deal with upgrade the pictures caught submerged and corrupted because of the medium dispersing and assimilation. Our technique is a solitary picture approach that doesn't require particular equipment or information about the submerged conditions or scene structure. It expands on the mixing of two pictures that are straightforwardly gotten from a shading redressed and white-adjusted rendition of the first debased picture. The two pictures to combination, just as their related weight maps, are characterized to advance the exchange of edges and shading complexity to the yield picture. To stay away from that the sharp weight map changes make ancient rarities in the low recurrence parts of the reproduced picture, we additionally adjust a multi scale combination technique. Our broad subjective and quantitative assessment uncovers that our upgraded pictures and recordings are described by better exposedness of the dull districts, improved worldwide difference, and edges sharpness. Our approval likewise demonstrates that our calculation is sensibly free of the camera settings, and improves the exactness of a few picture preparing applications.