Applications of machine vision in pharmaceutical technology: A review.

In this paper the proposed methodology is to recognize Facial expressions of human being by using Eigen Faces. For recognition of human expression, in this approach we consider the following steps: face detection, facial feature extraction and then facial expression classification. The proposed system is based on calculating the Euclidian distance for the Eigen Faces. In this approach we will consider all the seven basic emotions which are classified as happy, anger, sad, fear, and disgust, surprised, neutral. Here 50 facial expression images are considered and trained by Eigen Faces. Finally the featured Eigen Faces are compared with the test image. In our approach face recognition is done by “Principal component analysis (PCA)”. The experimental result gives 98.5% recognition rates for each emotion.

Facial Expression Recognition by Calculating Euclidian Distance for Eigen Faces Using PCA

Currency recognition technology focuses on searching and retrieving visible and hidden tags in monetary standards for a systematic classification. The recognition of the monetary standards and the conversion mechanism is implemented in order to decrease the human power to perceive the measure of the currency's currency value and to convert it to other standards without human supervision. The software interface suggested here could be utilized for various monetary standards (four standards are utilized in our project). Habitually, bank notes are either blurred or damaged; many have complicated recommendation to increase security. It makes the errand of perceiving the monetary standards troublesome. So, for this aspiration, it is predominant to choose the right functions and the right algorithm. The fundamental prerequisites for an algorithm that can be considered as practicable are simplicity, less complexity, high speed and efficiency.

Identification of Fake Notes and Denomination Recognition

humansareaffectedfrom many kind ofdiseases. Proper Medicationistheonlywaytoovercome fromsuchdiseases.Somedicinesbecome most important part of human life.Manufacturing of medicines is done in very large scale. Duringmanufacturing, thereare many kind of defects in tablet blister, defects are likebreakage, cracksetcpresentin tablets or capsules.There may be side-effects of these defected tablets or capsules due to variation in dosage when consumed. The manufactured tablets should be properly inspected before reaching to the public, so that they do not cause any side-effects.Manual inspection of such defects in tablet blister may be very challenging task.Image segmentation is an important technique for automation of visual inspection. Hence, it is important to propose some approaches to detect these defects in tablet blister. In literaturesurvey many researchers have proposed multiple procedures for identifying defects in tablet blister. In this research work we review allthe methods used to identify defects in tablets blister.

A Study on Inspection of Defective Tablet Blister Using Image Segmentation Techniques

Long-term adherence to medication is of critical importance for the successful management of chronic diseases. Objective tools to track oral medication adherence are either lacking, expensive, difficult to access, or require additional equipment. To improve medication adherence, cheap and easily accessible objective tools able to track compliance levels are necessary. A tool to monitor pill intake that can be implemented in mobile health solutions without the need for additional devices was developed. We propose a pill intake detection tool that uses digital image processing to analyze images of a blister to detect the presence of pills. The tool uses the Circular Hough Transform as a feature extraction technique and is therefore primarily useful for the detection of pills with a round shape. This pill detection tool is composed of two steps. First, the registration of a full blister and storing of reference values in a local database. Second, the detection and classification of taken and remaining pills in similar blisters, to determine the actual number of untaken pills. In the registration of round pills in full blisters, 100% of pills in gray blisters or blisters with a transparent cover were successfully detected. In the counting of untaken pills in partially opened blisters, 95.2% of remaining and 95.1% of taken pills were detected in gray blisters, while 88.2% of remaining and 80.8% of taken pills were detected in blisters with a transparent cover. The proposed tool provides promising results for the detection of round pills. However, the classification of taken and remaining pills needs to be further improved, in particular for the detection of pills with non-oval shapes.

/pdf/development-and-validation-of-a-digital-image-processing-1u9ecu1n.pdf

Development and Validation of a Digital Image Processing-Based Pill Detection Tool for an Oral Medication Self-Monitoring System

Automatic recognition and verification of defective tablet blisters using entropy based filtering and histogram processing

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Automatic recognition and verification of defective tablet blisters using entropy based filtering and histogram processing