The processing of an image of a moving plant is inadequate, for this reason, digital video processing must be incorporated, which allows the behavior of an algorithm to be analyzed over time. A method is presented that takes images of a plant with autonomous movement filmed on video; the frames are digitally processed and the information is used to generate animations. Our representation of the structure is derived from an analysis of the image where the plant is deformed; the projections of the movement of the plant are recovered from the video frames and are used as a basis to generate videograms in an animation based on key points taken from an image; Harris and Brisk algorithms are applied. The main plant used is the Mimosa Pudica. Once the frames have been obtained, correlation is proposed as a mechanism to find movement. The techniques are equally useful for any other moving plant such as carnivores or sunflowers.

In this paper we propose a method to tracking facial expressions. A system with two cameras is used to capture stereoscopic video sequences. The frames are acquired and analyzed by matching two stereoscopic frames through a correlation method that performs image processing to obtain a resulting frame, and then it is processed to recognize a human face by using the Viola and Jones (VJ) method. The face is located via the Nitzberg operator and it provides the feature points of the eyes, eyebrows, nose and mouth, which are introduced into a Backpropagation neural network that is capable of learning and classifying different types of facial expressions that make a person, feel such as: surprised, scared, unhappy, sad, mad and happy. Finally, the result of this process is recognition of facial expressions.

Abstract. This paper proposes a compressive sensing architecture for 128 × 128 pixels gray scale images. The proposed architecture is implemented in an FPGA platform. Due to speed and area advantages, the random numbers block generator is implemented using Linear Feedback Shift Register (LFSR) technique. The resulting random matrix is stored in a Random Access Memory (RAM) block. In addition, a second RAM is employed to store the sampled image. We also implement an Universal Asynchronous Receiver Transmitter (UART) to receive and transmit data. Besides the previous blocks, we design an Arithmetic/Logic Unit (ALU), which performs the operations in compressive sensing settings. In this way, a Unit Control (UC) based on a Mealy type state machine directs operations in our architecture. The purpose of the UC is threefold. First, the UC uses the UART to receive the sample image and store it in the corresponding RAM block. Second, the UC directs the matrix multiplication operation to the ALU and obtains the compressed image. Finally, the UART transmits the compressed image to a base station. The main characteristics of our architecture are the following: the maximum frequency of operation is 30 MHz, the power consumption is 37 mW, and the average time processing is 4.5 ms.