HTC Desire Z (also called Vision) Review: Pros, Cons, and Features of the QWERTY Slider

A large number of computer vision researchers focus each year on developing vision systems that enable machines to mimic human behavior. For example, some intelligent machines can use computer vision technology to simultaneously map their behavior, detect potential obstacles, and track their location. By applying computer vision to multimodal applications, complex operational processes can be automated and made more efficient. Here, the key challenge is to extract visual attributes from one or more data streams (also called modalities) with different shapes and dimensions by learning how to fuse the extracted heterogeneous features and project them into a common representation space, which is referred to as deep multimodal learning in this work.

HTC Desire Z (also called Vision) review

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Convolutional neural networks (CNNs or ConvNets) are a class of deep feed-forward neural networks whose main purpose is to extract spatial patterns from visual input signals [20, 22]. More specifically, such models tend to model a series of nonlinear transformations by generating very abstract and informative features from highly complex datasets. The main properties that distinguish CNNs from other models include their ability to capture local connectivity between units, to share weights across layers, and to block a sequence of hidden layers [4]. The architecture is based on hierarchical filtering operations, i.e., using convolution layers followed by activation functions, etc. Once the convolution layers are linearly stacked, the growth of the receptive field size (i.e., kernel size) of the neural layers can be simulated by a max-pooling operation, which implies a reduction in the spatial size of the feature map. After applying a series of convolution and pooling operations, the hidden representation learned from the model must be predicted. For this purpose, at least one fully connected layer (also called dense layer) is used that concatenates all previous activation maps.

For decades, visual SLAM (simultaneous localization and mapping) has been an active area of research in the robotics and computer vision communities [148, 150]. The challenge lies both in locating a robot and mapping its surrounding environment. Several methods have been reported to improve the mapping accuracy of real-time scenarios in unstructured and large-scale environments. Some of these methods include descriptor-based monocular cameras with ORB-SLAM [151], stereovision with ORB-SLAM2 [152], and photometric error-based methods such as LSD-SLAM [153] or DSO [154]. However, there are still many challenges facing these data-driven automated systems, particularly for intelligent perception and mapping. Some of these challenges are reflected in the fact that large amounts of data are required to train models. Therefore, large-scale datasets are required to ensure that systems produce the desired outcomes. As a result, more powerful feature extractors will require more parameters and, therefore, more learning data. For instance, Caesar et al. [155] demonstrated how generalization performance could be greatly improved when developing a multimodal dataset, called nuScenes, which is acquired by a wide range of remote sensors, including six cameras, five radars, and one LiDAR. The dataset consists of 1000 scenes in total, each about 20 s long and fully labeled with 3D bounding boxes that cover 23 classes and eight attributes. 2ff7e9595c