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In this paper we address the task of counting crop plants in a field using CNNs. The number of plants in an Unmanned Aerial Vehicle (UAV) image of the field is estimated using regression instead of classification. This avoids to need to know (or guess) the maximum expected number of plants. We also describe a method to extract images of sections or "plots" from an orthorectified image of the entire crop field. These images will be used for training and evaluation of the CNN.

We consider the problem of differentiating a multivariable function specified by noisy data. Following previous work for the single-variable case, we regularize the differentiation process, by formulating it as an inverse problem with an integration operator as the forward model. Total-variation regularization avoids the noise amplification of finite-difference methods, while allowing for discontinuous solutions. Unlike the single-variable case, we use an alternating directions, method of multipliers algorithm to provide greater efficiency for large problems.

Scene text detection is a critical prerequisite for many fascinating applications for vision-based intelligent robots. Existing methods detect texts either using the local information only or casting it as a semantic segmentation problem. They tend to produce a large number of false alarms or cannot separate individual words accurately. In this work, we present an elegant segmentation-aided text detection solution that predicts the word-level bounding boxes using an end-to-end trainable deep convolutional neural network.

In this paper, an image error concealment method based on joint local sparse representation and non-local similarity is proposed. The proposed method obtains an optimal sparse representation of an image patch, including missing pixels and known neighboring pixels for recovery purpose. At first, a pair of dictionary and a mapping function are simultaneously learned offline from a training data set.

Target re-identification across non-overlapping camera views is a challenging task due to variations in target appearance, illumination, viewpoint and intrinsic parameters of cameras. Brightness transfer function (BTF) was introduced for inter-camera color calibration, and to improve the performance of target re-identification methods. There have been several works based on BTFs, more specifically using weighted BTFs (WBTF), cumulative BTF (CBTF) and mean BTF (MBTF). In this paper, we present a novel method to model the ap-pearance variation across different camera views.

We present a novel No-Reference (NR) video quality assessment (VQA) algorithm that operates on the sparse represent- ation coefficients of local spatio-temporal (video) volumes. Our work is motivated by the observation that the primary visual cortex adopts a sparse coding strategy to represent visual stimulus. We use the popular K-SVD algorithm to construct spatio-temporal dictionary to sparsely represent local spatio-temporal volumes of natural videos.

We present a novel No-Reference (FR) video quality assessment
(VQA) algorithm that operates on the sparse representation
coefficients of local spatio-temporal (video) volumes.
Our work is motivated by the observation that the primary
visual cortex adopts a sparse coding strategy to represent
visual stimulus. We use the popular K-SVD algorithm to construct
spatio-temporal dictionaries to sparsely represent local
spatio-temporal volumes of natural videos. We empirically
demonstrate that the histogram of the sparse representations

We present a novel No-Reference (FR) video quality assessment
(VQA) algorithm that operates on the sparse representation
coefficients of local spatio-temporal (video) volumes.
Our work is motivated by the observation that the primary
visual cortex adopts a sparse coding strategy to represent
visual stimulus. We use the popular K-SVD algorithm to construct
spatio-temporal dictionaries to sparsely represent local
spatio-temporal volumes of natural videos. We empirically
demonstrate that the histogram of the sparse representations