Variations In Visual Appearance Research Articles

Optical properties of tilted surfaces in material jetting

Material jetting (MJT) is a promising additive manufacturing (AM) technique that offers high-quality appearance reproduction. Considering the limited selection of aesthetic materials available for MJT technology, a thorough optical analysis is required to determine their optimal application. This study focuses on the prominent appearance attributes in MJT and the role of texture variation due to different build orientations (BOs). For this purpose, tilted surfaces were manufactured in a direction ranging from 0° to 90° degrees at 15° intervals. The spectral reflectance, absorbance, transmittance, color difference, gloss, haze, scattering, and texture of MJT objects with varying BOs were investigated. To illustrate the variation in visual appearance and texture of the studied surfaces, the optical properties were rendered to three-dimensional (3D) spherical models. Further, the interaction of the appearance of 3D printing interacts with the quality improvement of AM surfaces is discussed. The results indicated that BO significantly affected the surface texture and layer orientation, leading to variation in surface quality by impacting all studied appearance attributes. Furthermore, tilted MJT surfaces exhibited more variation in reflectance than transmittance. As a result of the Kendall rank correlation coefficient and principal component analysis (PCA), light transmittance through parts manufactured at BO 75° showed the largest variation compared to 0°. Regarding light reflectance, 60° showed more variation among studied angles. The results of gloss, haze, and scattering studies revealed the significance of the measurement direction due to the role of microsurface normals in the surface texture.

Appearance evaluation of digital materials in material jetting

Material jetting (MJT) products have a high subjective appearance quality in additive manufacturing (AM). MJT objects are typically manufactured from multiple layers of semi-transparent photo resins to achieve the desired appearance. This study explores the optical properties of photopolymer plates in monolayer and bilayer combinations. The role of the white substrate as the background plate in bilayer configurations is discussed concerning appearance reproduction in MJT parts. For this purpose, spectral reflectance, transmittance, absorbance, color difference, and texture were investigated for MJT objects with different plate combinations. The primary digital materials in MJT were further investigated for haze, gloss, scattering variations, and their corresponding bidirectional reflectance distribution functions (BRDFs). The optical properties of the studied surfaces were then applied to three-dimensional (3D) models to illustrate the variation in visual appearance and texture. According to the results, it was inevitable to use a white background plate for color reproduction. Therefore, increasing the layer thickness did not improve color fidelity. Due to the role of microfacet normals in the surface texture, gloss, haze, and scattering, results followed the same analysis pattern but depended on the measurement direction.

Digital taxonomist: Identifying plant species in community scientists’ photographs

Automatic identification of plant specimens from amateur photographs could improve species range maps, thus supporting ecosystems research as well as conservation efforts. However, classifying plant specimens based on image data alone is challenging: some species exhibit large variations in visual appearance, while at the same time different species are often visually similar; additionally, species observations follow a highly imbalanced, long-tailed distribution due to differences in abundance as well as observer biases. On the other hand, most species observations are accompanied by side information about the spatial, temporal and ecological context. Moreover, biological species are not an unordered list of classes but embedded in a hierarchical taxonomic structure. We propose a multimodal deep learning model that takes into account these additional cues in a unified framework. Our Digital Taxonomist is able to identify plant species in photographs better than a classifier trained on the image content alone, the performance gained is over 6 percent points in terms of accuracy.

Learning Visual Instance Retrieval from Failure: Efficient Online Local Metric Adaptation from Negative Samples.

Existing visual instance retrieval (VIR) approaches attempt to learn a faithful global matching metric or discriminative feature embedding offline to cover enormous visual appearance variations, so as to directly use it online on various unseen probes for retrieval. However, their requirement for a huge set of positive training pairs is very demanding in practice and the performance is largely constrained for the unseen testing samples due to the severe data shifting issue. In contrast, this paper advocates a different paradigm: part of the learning can be performed online but with nominal costs, so as to achieve online metric adaptation for different query probes. By exploiting easily-available negative samples, we propose a novel solution to achieve the optimal local metric adaptation effectively and efficiently. The insight of our method is the local hard negative samples can actually provide tight constraints to fine tune the metric locally. Our local metric adaptation method is generally applicable to be used on top of any offline-learned baselines. In addition, this paper gives in-depth theoretical analyses of the proposed method to guarantee the reduction of the classification error both asymptotically and practically. Extensive experiments on various VIR tasks have confirmed our effectiveness and superiority.

Person Re-Identification With Joint Verification and Identification of Identity-Attribute Labels

One of the major challenges in person Re-Identification (ReID) is the inconsistent visual appearance of a person. Current works on visual feature and distance metric learning have achieved significant achievements, but still suffer from the limited robustness to pose variations, viewpoint changes, etc. This makes person ReID among multiple cameras still challenging. This work is motivated to learn mid-level human attributes which are robust to visual appearance variations and could be used as efficient features for person matching. We propose a supervised multi-task learning framework which considers attribute label information with joint identification-verification network to simultaneously learn an attribute-semantic and identity-discriminative feature representation. Specifically, this framework adopts the part-based deep neural network and learn three different tasks simultaneously: person identification, person verifications and attribute identification, so as to discover and capture concurrently complementary discriminative information about a person image from global and local image features and mid-level attribute features in one deep neural network. With the multi-task learning architecture, we obtain a discriminative model that reaches a synergy in distinguishing different person images, as manifested with the competitive accuracy on three person ReID datasets: Market1501, DukeMTMC-reID and VIPeR.

Multi-type attributes driven multi-camera person re-identification

One of the major challenges in person Re-Identification (ReID) is the inconsistent visual appearance of a person. Current works on visual feature and distance metric learning have achieved significant achievements, but still suffer from the limited robustness to pose variations, viewpoint changes, etc., and the high computational complexity. This makes person ReID among multiple cameras still challenging. This work is motivated to learn mid-level human attributes which are robust to visual appearance variations and could be used as efficient features for person matching. We propose a weakly supervised multi-type attribute learning framework which considers the contextual cues among attributes and progressively boosts the accuracy of attributes only using a limited number of labeled data. Specifically, this framework involves a three-stage training. A deep Convolutional Neural Network (dCNN) is first trained on an independent dataset labeled with attributes. Then it is fine-tuned on another dataset only labeled with person IDs using our defined triplet loss. Finally, the updated dCNN predicts attribute labels for the target dataset, which is combined with the independent dataset for the final round of fine-tuning. The predicted attributes, namely deep attributes exhibit promising generalization ability across different datasets. By directly using the deep attributes with simple Cosine distance, we have obtained competitive accuracy on four person ReID datasets. Experiments also show that a simple distance metric learning modular further boosts our method, making it outperform many recent works.

Multiple metric learning based on bar-shape descriptor for person re-identification

The robust structural feature extraction and similarity measure play critical roles in person re-identification. This paper presents a novel algorithm named Multiple Metric Learning based on Bar-shape Descriptor (MMLBD) for person re-identification. Specifically, we first propose a new Multiple Bar-shape Descriptor that can take full account of the spatial correlation between the center points and their adjacent points on different directions. It captures further histogram features based on a novel color difference weight factors with an overlapping sliding window, which can depict the local variations and consistency in the whole image. The similarity and dissimilarity of samples are used to train the weight factor of features and an optimal subspace could be obtained at the same time. Next, we provide an effective multiple metric learning method fusing two-channel bar-shape structural features via the optimal similarity pairwise measure obtained by a dissimilarity matrix. This measure can fully mine the discriminative information and eliminate redundancy in the similar features, which make the MMLBD simple and effective. Finally, evaluation experiments on the i_LIDS, CAVIAR4REID and WARD data-sets are carried out, which compare the proposed MMLBD with the corresponding methods. Experimental results demonstrate that the MMLBD is more effective and robust against visual appearance variations.

Subclass linear discriminant analysis for video-based face recognition

We present a novel subclass Linear Discriminant Analysis algorithm for feature extraction that copes with the severe pose, expression and illumination changes present in faces extracted from far-field video streams with subjects unconstrained in their motion and uncooperative to the system. Our novelty lies on the efficient automatic generation of subclasses from the gallery faces, by exploiting their different visual appearance and not constrained by their numbers per class. The proposed feature extraction algorithm is integrated in our complete face recognition system, with modules for preprocessing, classification, and decision fusion. We demonstrate the capability of the new algorithm to automatically generate discriminable subclasses and the resulting improved classification accuracy on a challenging video-based dataset, comprising low quality and resolution faces, as well as large variations in visual appearance. Our results indicate superior recognition rate compared to any systems in the CLEAR 2007 evaluation, running on that dataset.

Estimation and monitoring of product aesthetics: application to manufacturing of “engineered stone” countertops

A new machine vision approach for quantitatively estimating and monitoring the appearance and aesthetics of manufactured products is presented. The approach is composed of three steps: (1) wavelet-based textural feature extraction from product images, (2) estimation of measures of the product appearance through subspace projection of the textural features, and (3) monitoring of the appearance in the latent variable subspace of the textural features. The methodology is specifically designed to treat the stochastic nature of the visual appearance of many manufactured products. This nondeterministic aspect of product appearance has been an obstacle for the success of machine vision in many industries. The emphasis of this approach is on the consistent and quantitative estimation of continuous variations in visual appearance rather than on classification into discrete classes. This allows for the on-line monitoring and the eventual feedback control of product appearance. This approach is successfully applied to the estimation and monitoring of the aesthetic quality of manufactured stone countertops.

Visual positioning of previously defined ROIs on microscopic slides

In microscopy, regions of interest are usually much smaller than the whole slide area. Various microscopy related medical applications, such as telepathology and computer aided diagnosis, are liable to benefit greatly from microscope auto positioning on previously defined regions of interest. In this paper, we present a method for image-based auto positioning on a microscope slide. The method is based on localization of a microscopic query image using a previously acquired slide map. It uses geometric hashing, a highly efficient technique drawn from the object recognition field. The algorithm exhibits high tolerance to possible variations in visual appearance due to slide rotations, scaling and illumination changes. Experimental results indicate high reliability of the algorithm.