Enhanced Image Captioning with Color Recognition Using Deep Learning Methods

Automatically describing the content of an image is an interesting and challenging task in artificial intelligence. In this paper, an enhanced image captioning model—including object detection, color analysis, and image captioning—is proposed to automatically generate the textual descriptions of images. In an encoder–decoder model for image captioning, VGG16 is used as an encoder and an LSTM (long short-term memory) network with attention is used as a decoder. In addition, Mask R-CNN with OpenCV is used for object detection and color analysis. The integration of the image caption and color recognition is then performed to provide better descriptive details of images. Moreover, the generated textual sentence is converted into speech. The validation results illustrate that the proposed method can provide more accurate description of images.

Two-color super-resolution localization microscopy via joint encoding of emitter location and color

Multi-color super-resolution localization microscopy (SRLM) provides great opportunities for studying the structural and functional details of biological samples. However, current multi-color SRLM methods either suffer from medium to high crosstalk, or require a dedicated optical system and a complicated image analysis procedure. To address these problems, here we propose a completely different method to realize multi-color SRLM. This method is built upon a customized RGBW camera with a repeated pattern of filtered (Red, Green, Blue and Near-infrared) and unfiltered (White) pixels. With a new insight that RGBW camera is advantageous for color recognition instead of color reproduction, we developed a joint encoding scheme of emitter location and color. By combing this RGBW camera with the joint encoding scheme and a simple optical set-up, we demonstrated two-color SRLM with ∼20 nm resolution and < 2% crosstalk (which is comparable to the best reported values). This study significantly reduces the complexity of two-color SRLM (and potentially multi-color SRLM), and thus offers good opportunities for general biomedical research laboratories to use multi-color SRLM, which is currently mastered only by well-trained researchers.

Color recognition of dermatoscopic images of skin neoplasms

The problem of determining the colors of dermatoscopic images of skin neoplasms using computer technologies is considered. Based on the proposed model, a program for recognizing the colors of the studied areas of neoplasm has been developed. The adequacy of this model was tested experimentally. This work is designed to increase the reliability of the diagnosis of skin neoplasms.

Rubik’s Cube Solver

: Rubik’s cube is considered to be the most interesting and challenging problem in the world. It is a 3D combination puzzle that was originally called the Magic cube. It has only one correct solution out of the 43quintillion other possibilities. Building an application to solve such a puzzle is a very challenging task. In this paper, the design of such a Rubik’s cube solver website using Color Recognition has been discussed. This paper includes the overall process flow for solving the Rubik cube [1]. Our website is designed in such a way that when it will receive a scrambled Rubik’s Cube, it will visually evaluate it, will determine how that Rubik’s cube can be solved through manipulations and will provide a guide of the solution to the specific user. We have used Color recognition for detecting the initial orientation of the cube. And Segmentation is used to obtain the color pattern of the scrambled cube.

Operating Virtual Keyboard and Mouse Using Hand Gestures

This project promotes an approach for the Human Computer Interaction (HCI) where cursor movement can be controlled using a real-time camera, it is an alternative to the current methods including manual input of buttons or changing the positions of a physical computer mouse. Instead, it utilizes a camera and computer vision technology to control various mouse events and is capable of performing every task that the physical computer mouse can. The Virtual Mouse color recognition program will constantly acquiring real-time images where the images will undergone a series of filtration and conversion. Whenever the process is complete, the program will apply the image processing technique to obtain the coordinates of the targeted colors position from the converted frames. After that, it will proceed to compare the existing colors within the frames with a list of color combinations, where different combinations consists of different mouse functions. If the current colors combination found a match, the program will execute the mouse function, which will be translated into an actual mouse function to the users' machine.

Graphical user interface for the patterns detection in wine crops

Considering that our country has an important participation in the grape productive sector for this reason it is one of the crops with the best opportunity areas for the implementation of this technology type. In this paper the design and development of a Graphical User Interface (GUI) generated in the MATLAB programming environment is exposed, through which the pictures acquisition and process from interest information is carried out to implement patter recognition strategies in the wine crops agroindustrial sector to monitor and generate a timely diagnostic of its currently status. The GUI has a section than allows the pictures acquisition in real time to later capture the information to be processed and through the application of filters and color recognition techniques on the crop leaf (study object) it’s processed to establish a diagnostic, which will allow the user to apply the appropriate measures contributing in the best way to a crop optimal development.

Pengenalan Permainan Warna Melalui Konsep Senyawa Polar dan Non Polar

Introduction to Color Games Through the Concept of Polar and Non Polar Compounds. This research was conducted to provide a new learning experience by adapting the concept of color recognition combined with simple science (polar and non polar). This study uses quantitative research approach with one-shot case study type. The results of a simple experimental process conducted in the learning process get positive data that is 88% or 15 children get a very good score and 12% or 2 children get a good score. Based on these results, similar activities can be carried out to introduce children to the concept of mixing colors adapting simple science in learning activities.