Bildigenkänning och jämförelse är ett ämne som har varit i fokus under en lång tid inom datavetenskap. Många företag har försökt att skapa produkter, som utnyttjar olika lösningar för att känna igen objekt och människor. Dock har ingen lyckats skapa en lösning som kan göra detta felfritt. Lifesum vill ha en lösning till deras kaloriräknarapplikation. Denna ska erbjuda användaren möjligheten att fotografera en maträtt, för att sedan kunna ta fram vilken maträtt som bilden illustrerar. Histogramjämförelse är ett av lösningsalternativen, dock inte den mest optimala bildjämförelsealgoritmen. Att använda en algoritm som utnyttjar nyckelpunktsdetektion är den mest optimala lösningen, om träning av algoritmen är ett alternativ. En av idéerna för att öka precisionen är att låta användaren välja mellan de fem bästa maträtterna som algoritmen rekommenderar. På så sätt ökar man sannolikheten att maträtten som söks är en av de rekommenderade maträtterna. Framtida arbeten inom detta ämne kan involvera forskning i hur träning utav HOG, Histogram of Oriented Gradients, algoritmen skulle fungera. Detta för att få ett bättre resultat som låter FLANN, Fast Approximate Nearest Neighbor Search Library, algoritmen arbeta snabbare. / Image recognition and comparison is a topic that has been in focus for a long time within computer science. Many companies have tried to create products that use different solutions to recognize objects and people. However, none of these companies have managed to create a solution that can do this flawlessly. Lifesum want a solution to their calorie counting application. This will offer the user the opportunity to take a picture of a dish and then be able to retrieve which dish the image illustrates. Histogram comparison is one solution to this problem, thought not the most optimal one. Using an algorithm that uses keypoint detection is the most optimal solution, if training of the algorithm is an option. One of the ideas to improve the precision is to allow the user to choose between the five best dishes that the algorithm recommends. In this way one increase the probability of that the wanted dish is one of the recommended dishes. Future work in this topic can involve researching on how training the HOG, Histogram of Oriented Gradients, algorithm would work, to get a better result that could let the FLANN, Fast Approximate Nearest Neighbor Search Library, algorithm work faster.

This work describes the development of legislative provisions relating to the issue of animal products as food, issued in the CR and the EU. This work also reports their implementation in food businesses, particularly in terms of data provided on product labels. During this work product labels were collected in selected chain stores, documented by photography and the obtained results were compared with the applicable requirements of the CR. The observed data of this study are also compared with the requirements of Act No. 110/1997 Coll. Food, and Decree No. 113/2005 Coll. Individual products of the investigated area (meat and dairy products) are also compared with each other (Point method and Simplified Point Method) and in terms of significance, consumer attention and other appurtenances associated with data on product labels were surveyed. The results revealed a strong dominance of Czech manufacturers on our market in categories of both milk and meat products. In terms of characteristics that producers are obliged to provide, no significant errors were found. If the products lacked any indication, it was usually an indication of dietary (nutritional) value for both meat and dairy products. Manufacturers of the products studied provided a large number of optional information (logos, etc.). We conclude they should rather focus on some of consumer-preferred emblems, for there is high number of them and they seem difficult to follow by the customers. Most consumers prefer the Klasa and the ?Český výrobek? (Czech product) logos.